Techniques for decentralized load balancing

ABSTRACT

Various embodiments are generally directed to decentralized load balancing in a host cluster utilized to coordinate performance of processing tasks in a workload, such as via service agents and/or host instances included in the host cluster, for instance. Some embodiments are particularly directed to a set of service agents on one or more host instances that utilize a shared cache to coordinate among themselves to automatically balance a workload without a centralized controller or a centralized load balancer. In one or more embodiments, a set of service agents may automatically and cooperatively balance a workload among themselves using the shared cache.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims the benefit ofpriority under 35 U.S.C. § 120 to, U.S. patent application Ser. No.15/852,467 filed Dec. 22, 2017, the entirety of which is incorporatedherein by reference. U.S. patent application Ser. No. 15/852,467 claimsthe benefit of priority under 35 U.S.C. § 119(e) to U.S. ProvisionalApplication Ser. No. 62/460,360 filed Feb. 17, 2017, the entirety ofwhich is incorporated herein by reference.

SUMMARY

This summary is not intended to identify only key or essential featuresof the described subject matter, nor is it intended to be used inisolation to determine the scope of the described subject matter. Thesubject matter should be understood by reference to appropriate portionsof the entire specification of this patent, any or all drawings, andeach claim.

Various embodiments described herein may include an apparatus comprisinga processor and a storage to store instructions that, when executed bythe processor, cause the processor to perform operations comprising:initialize a first timer associated with a first service agent in a setof service agents implemented by a first host instance, the set ofservice agents comprising two or more service agents, wherein eachservice agent in the set of service agents is in a state of one or morestates, the one or more states including a sleep state and an activestate; transition the first service agent from the sleep state to theactive state in response to expiration of the first timer; determine,via a shared cache, that the first service agent is not one of one ormore agents in the active state for a first task type based on a datastructure in the shared cache that indicates the one or more agents inthe active state for the first task type, the shared cache accessible toa cluster of two or more host instances, the cluster of host instancesincluding the first host instance and a second host instance; identify avalue of a first timestamp of one or more timestamps in the shared cachebased on an association of the first timestamp with the first task typein the shared cache, the first timestamp associated with performance ofa task of the first task type by one of the one or more agents in theactive state for the first task type and implemented by the second hostinstance; determine a time elapsed between the value of the firsttimestamp and a current time, wherein when the time elapsed is above aservice threshold, the processor to perform operations comprising setthe first service agent as one of the one or more agents in the activestate for the first task type in the data structure in the shared cacheand update the value of the first timestamp associated with the firsttask type in the shared cache, and wherein when the time elapsed is ator below the service threshold, the processor to perform operationscomprising: identify a number of agents in the active state associatedwith the second host instance via the shared cache, place the firstservice agent in the sleep state when a difference between the number ofagents in the active state associated with the second host instance anda number of agents in the active state associated with the first hostinstance is at or below a load difference threshold, and set the firstservice agent as one of the one or more agents in the active state forthe first task type in the shared cache and update the value of thefirst timestamp in the shared cached when the difference between thenumber of agents in the active state associated with the second hostinstance and the number of agents in the active state associated withthe first host instance is above the load difference threshold.

In some embodiments, the processor may be caused to perform operationscomprising perform a respective task of the first task type with thefirst service agent when the first service agent is identified as one orthe one or more agents in the active state for the first task type inthe data structure in the shared cache.

In one or more embodiments, the processor may be caused to performoperations comprising update the value of the first timestamp in theshared cache in response to performance of the respective task of thefirst task type.

In various embodiments, the first timestamp may be associated with thefirst task type and one of the one or more agents in the active statefor the first task type, and wherein setting the first service agent asone of the one or more agents in the active state for the first tasktype associates the first timestamp with the first service agent.

In some embodiments, the processor may be caused to perform operationscomprising: initialize a second timer associated with a second serviceagent in the set of service agents implemented by the first hostinstance; transition the second service agent from the sleep state tothe active state in response to expiration of the second timer;determine the second service agent is one of one or more agents in theactive state for a second task type via the shared cache based on asecond data structure in the shared cache that indicates the one or moreagents in the active state for the second task type; update a value of asecond timestamp of the one or more timestamps in the shared cache to asecond current time, the second timestamp associated with the secondtask type and the second service agent, wherein the value of the secondtimestamp that is updated is associated with performance of a respectivetask of the second task type by the second service agent; and performthe respective task of the second task type with the second serviceagent.

In one or more embodiments, the first task type and the second task typemay comprise a same task type.

In various embodiments, the first service agent may comprise a metriccollecting agent and the first task type comprising a resource metriccollection service to monitor dynamic metrics associated with health ofa software system.

In some embodiments, the processor may be caused to perform operationscomprising: comparing processing capabilities of hardware resourcesavailable to the first host instance to processing capabilities ofhardware resources available to the second host instance; anddetermining the load difference threshold based on the comparison,wherein when the processing capabilities of the hardware resourcesavailable to the first host are equal to the processing capabilities ofthe hardware resources available to the second host, the load differencethreshold is determined to be zero.

In one or more embodiments, the service threshold may comprise a timeequal to twice a service interval, wherein the service intervalcomprises an amount of time between initialization and expiration of thefirst timer.

In some embodiments, the task of the first task type may be included ina workload comprising a plurality of tasks, wherein the cluster of hostinstances utilizes the shared cache to balance the plurality of tasks inthe workload among the host instances in the cluster without acentralized controller or load balancer.

Various embodiments described herein may include a computer-implementedmethod, comprising: initializing a first timer associated with a firstservice agent in a set of service agents implemented by a first hostinstance, the set of service agents comprising two or more serviceagents, wherein each service agent in the set of service agents is in astate of one or more states, the one or more states including a sleepstate and an active state; transitioning the first service agent fromthe sleep state to the active state in response to expiration of thefirst timer; determining, via a shared cache, that the first serviceagent is not one of one or more agents in the active state for a firsttask type based on a data structure in the shared cache that indicatesthe one or more agents in the active state for the first task type, theshared cache accessible to a cluster of two or more host instances, thecluster of host instances including the first host instance and a secondhost instance; identifying a value of a first timestamp of one or moretimestamps in the shared cache based on an association of the firsttimestamp with the first task type in the shared cache, the firsttimestamp associated with performance of a task of the first task typeby one of the one or more agents in the active state for the first tasktype and implemented by the second host instance; determining a timeelapsed between the value of the first timestamp and a current time,wherein when the time elapsed is above a service threshold, setting thefirst service agent as one of the one or more agents in the active statefor the first task type in the data structure in the shared cache andupdate the value of the first timestamp associated with the first tasktype in the shared cache, and wherein when the time elapsed is at orbelow the service threshold: identifying a number of agents in theactive state associated with the second host instance via the sharedcache, placing the first service agent in the sleep state when adifference between the number of agents in the active state associatedwith the second host instance and a number of agents in the active stateassociated with the first host instance is at or below a load differencethreshold, and setting the first service agent as one of the one or moreagents in the active state for the first task type in the shared cacheand update the value of the first timestamp in the shared cached whenthe difference between the number of agents in the active stateassociated with the second host instance and the number of agents in theactive state associated with the first host instance is above the loaddifference threshold.

Some embodiments may include performing a respective task of the firsttask type with the first service agent when the first service agent isidentified as one or the one or more agents in the active state for thefirst task type in the data structure in the shared cache.

One or more embodiments may comprise updating the value of the firsttimestamp in the shared cache in response to performance of therespective task of the first task type.

In various embodiments, the first timestamp may be associated with thefirst task type and one of the one or more agents in the active statefor the first task type, and wherein setting the first service agent asone of the one or more agents in the active state for the first tasktype associates the first timestamp with the first service agent.

Some embodiments may include: initializing a second timer associatedwith a second service agent in the set of service agents implemented bythe first host instance; transitioning the second service agent from thesleep state to the active state in response to expiration of the secondtimer; determining the second service agent is one of one or more agentsin the active state for a second task type via the shared cache based ona second data structure in the shared cache that indicates the one ormore agents in the active state for the second task type; updating avalue of a second timestamp of the one or more timestamps in the sharedcache to a second current time, the second timestamp associated with thesecond task type and the second service agent, wherein the value of thesecond timestamp that is updated is associated with performance of arespective task of the second task type by the second service agent; andperforming the respective task of the second task type with the secondservice agent.

In one or more embodiments, the first task type and the second task typemay comprise a same task type.

In some embodiments, the first service agent may comprise a metriccollecting agent and the first task type comprising a resource metriccollection service to monitor dynamic metrics associated with health ofa software system.

Various embodiments may include one or more of the following: comparingprocessing capabilities of hardware resources available to the firsthost instance to processing capabilities of hardware resources availableto the second host instance; and determining the load differencethreshold based on the comparison, wherein when the processingcapabilities of the hardware resources available to the first host areequal to the processing capabilities of the hardware resources availableto the second host, the load difference threshold is determined to bezero.

In one or more embodiments, the service threshold may comprise a timeequal to twice a service interval, wherein the service intervalcomprises an amount of time between initialization and expiration of thefirst timer.

In various embodiments, the task of the first task type may be includedin a workload comprising a plurality of tasks, wherein the cluster ofhost instances utilizes the shared cache to balance the plurality oftasks in the workload among the host instances in the cluster without acentralized controller or load balancer.

Some embodiments described herein may include a computer-program producttangibly embodied in a non-transitory machine-readable storage medium,the computer-program product including instructions operable to cause aprocessor to perform operations comprising: initialize a first timerassociated with a first service agent in a set of service agentsimplemented by a first host instance, the set of service agentscomprising two or more service agents, wherein each service agent in theset of service agents is in a state of one or more states, the one ormore states including a sleep state and an active state; transition thefirst service agent from the sleep state to the active state in responseto expiration of the first timer; determine, via a shared cache, thatthe first service agent is not one of one or more agents in the activestate for a first task type based on a data structure in the sharedcache that indicates the one or more agents in the active state for thefirst task type, the shared cache accessible to a cluster of two or morehost instances, the cluster of host instances including the first hostinstance and a second host instance; identify a value of a firsttimestamp of one or more timestamps in the shared cache based on anassociation of the first timestamp with the first task type in theshared cache, the first timestamp associated with performance of a taskof the first task type by one of the one or more agents in the activestate for the first task type and implemented by the second hostinstance; determine a time elapsed between the value of the firsttimestamp and a current time, wherein when the time elapsed is above aservice threshold, the instructions to cause the processor to performoperations comprising set the first service agent as one of the one ormore agents in the active state for the first task type in the datastructure in the shared cache and update the value of the firsttimestamp associated with the first task type in the shared cache, andwherein when the time elapsed is at or below the service threshold, theinstructions to cause the processor to perform operations comprising:identify a number of agents in the active state associated with thesecond host instance via the shared cache, place the first service agentin the sleep state when a difference between the number of agents in theactive state associated with the second host instance and a number ofagents in the active state associated with the first host instance is ator below a load difference threshold, and set the first service agent asone of the one or more agents in the active state for the first tasktype in the shared cache and update the value of the first timestamp inthe shared cached when the difference between the number of agents inthe active state associated with the second host instance and the numberof agents in the active state associated with the first host instance isabove the load difference threshold.

One or more embodiments may include instructions operable to cause theprocessor to perform operations comprising perform a respective task ofthe first task type with the first service agent when the first serviceagent is identified as one or the one or more agents in the active statefor the first task type in the data structure in the shared cache.

Various embodiments may include instructions operable to cause theprocessor to perform operations comprising update the value of the firsttimestamp in the shared cache in response to performance of therespective task of the first task type.

In some embodiments, the first timestamp associated with the first tasktype and one of the one or more agents may be in the active state forthe first task type, and wherein setting the first service agent as oneof the one or more agents in the active state for the first task typeassociates the first timestamp with the first service agent.

One or more embodiments may include instructions operable to cause theprocessor to perform operations comprising: initialize a second timerassociated with a second service agent in the set of service agentsimplemented by the first host instance; transition the second serviceagent from the sleep state to the active state in response to expirationof the second timer; determine the second service agent is one of one ormore agents in the active state for a second task type via the sharedcache based on a second data structure in the shared cache thatindicates the one or more agents in the active state for the second tasktype; update a value of a second timestamp of the one or more timestampsin the shared cache to a second current time, the second timestampassociated with the second task type and the second service agent,wherein the value of the second timestamp that is updated is associatedwith performance of a respective task of the second task type by thesecond service agent; and perform the respective task of the second tasktype with the second service agent.

In various embodiments, the first task type and the second task type maycomprise a same task type.

In some embodiments, the first service agent may comprise a metriccollecting agent and the first task type comprising a resource metriccollection service to monitor dynamic metrics associated with health ofa software system.

One or more embodiments may include instructions operable to cause theprocessor to perform operations comprising: comparing processingcapabilities of hardware resources available to the first host instanceto processing capabilities of hardware resources available to the secondhost instance; and determining the load difference threshold based onthe comparison, wherein when the processing capabilities of the hardwareresources available to the first host are equal to the processingcapabilities of the hardware resources available to the second host, theload difference threshold is determined to be zero.

In various embodiments, the service threshold may comprise a time equalto twice a service interval, wherein the service interval comprises anamount of time between initialization and expiration of the first timer.

In some embodiments, the task of the first task type may be included ina workload comprising a plurality of tasks, wherein the cluster of hostinstances utilizes the shared cache to balance the plurality of tasks inthe workload among the host instances in the cluster without acentralized controller or load balancer.

One or more embodiments described herein may include a systemcomprising: a cluster having a plurality of host instances, each hostinstance in the cluster to implement one or more service agents, the oneor more service agents to perform tasks included in a workload; a sharedcache utilized by the one or more service agents of the plurality ofhosts instances to automatically balance processing of the tasks in theworkload among the host instances in the cluster without a centralizedcontroller or a load balancer; a first service agent of the one or moreservice agents implemented by a first host instance in the cluster ofhost instances to perform a task in the workload based on a datastructure in the shared cache that indicates the first service agent isan active agent for the task; a second service agent of the one or moreservice agents implemented by a second host instance in the cluster ofhost instances to: transition from a sleep state to an active state inresponse to expiration of a timer; identify a number of active serviceagents associated with the first host instance based on the datastructure in the shared cache; compute a difference between the numberof active service agents associated with the first host instance and anumber of active service agents associated with the second hostinstance; compare the difference between the number of active serviceagents associated with the first host instance and the number of activeservice agents associated with the second host instance to a loaddifference threshold; and update the data structure in the shared cacheto indicate that the second service agent is the active agent for thetask to reduce a portion of the workload performed by the first serviceagent of the one or more service agents implemented by the first hostinstance in the cluster of host instances when the difference betweenthe number of active service agents associated with the first hostinstance and the number of active service agents associated with thesecond host instance exceeds the load difference threshold.

In some embodiments, the first host instance and the second hostinstance may have local access to a synchronized copy of the sharedcached.

In various embodiments, the second service agent initializes a timer andtransitions into the sleep state when the difference between the numberof active service agents associated with the first host instance and thenumber of active service agents associated with the second host instancedoes not exceed the load difference threshold.

In one or more embodiments, the second service agent performs a secondtask in the workload and updates a timestamp in the shared cached to acurrent time based on performance of the second task.

In some embodiments, the data structure may include an associationbetween the timestamp and a type of the task.

In various embodiments, the first service agent comprising a metriccollecting agent and wherein the task includes a portion of a resourcemetric collection service that monitors dynamic metrics associated withhealth of a software system.

In one or more embodiments, the first service agent may update atimestamp in the shared cached to indicate completion of the task.

In some embodiments, the first service agent may identify the task forperformance based on a task queue in the shared cache.

In various embodiments, the load difference threshold may be determinedbased on a comparison of processing capabilities of hardware resourcesavailable to the first host instance to processing capabilities ofhardware resources available to the second host instance.

In one or more embodiments, the load difference may be set to zero basedon the comparison indicating the processing capabilities of the hardwareresources available to the first host instance are equal to theprocessing capabilities of the hardware resources available to thesecond host instance.

Some embodiments described herein may include a computer-implementedmethod, comprising: implementing one or more service agents with eachhost instance in a cluster having a plurality of host instances, the oneor more service agents to perform tasks included in a workload;utilizing a shared cache with the one or more service agents of theplurality of hosts instances to automatically balance processing of thetasks in the workload among the host instances in the cluster without acentralized controller or a load balancer; performing a task in theworkload with a first service agent based on a data structure in theshared cache that indicates the first service agent is an active agentfor the task, the first service agent of the one or more service agentsimplemented by a first host instance in the cluster of host instances;implementing a second service agent of the one or more service agentswith a second host instance in the cluster of host instances, the secondservice agent for: transitioning from a sleep state to an active statein response to expiration of a timer; identifying a number of activeservice agents associated with the first host instance based on the datastructure in the shared cache; computing a difference between the numberof active service agents associated with the first host instance and anumber of active service agents associated with the second hostinstance; comparing the difference between the number of active serviceagents associated with the first host instance and the number of activeservice agents associated with the second host instance to a loaddifference threshold; and updating the data structure in the sharedcache to indicate that the second service agent is the active agent forthe task to reduce a portion of the workload performed by the firstservice agent of the one or more service agents implemented by the firsthost instance in the cluster of host instances when the differencebetween the number of active service agents associated with the firsthost instance and the number of active service agents associated withthe second host instance exceeds the load difference threshold.

In various embodiments, the first host instance and the second hostinstance may have local access to a synchronized copy of the sharedcached.

In one or more embodiments, the second service agent may initialize atimer and transition into the sleep state when the difference betweenthe number of active service agents associated with the first hostinstance and the number of active service agents associated with thesecond host instance does not exceed the load difference threshold.

In some embodiments, the second service agent may perform a second taskin the workload and updates a timestamp in the shared cached to acurrent time based on performance of the second task.

In various embodiments, the data structure may include an associationbetween the timestamp and a type of the task.

In one or more embodiments, the first service agent may comprise ametric collecting agent and wherein the task includes a portion of aresource metric collection service that monitors dynamic metricsassociated with health of a software system.

In some embodiments, the first service agent may update a timestamp inthe shared cached to indicate completion of the task.

In various embodiments, the first service agent may identify the taskfor performance based on a task queue in the shared cache.

In one or more embodiments, the load difference threshold may bedetermined based on a comparison of processing capabilities of hardwareresources available to the first host instance to processingcapabilities of hardware resources available to the second hostinstance.

In some embodiments, the load difference may be set to zero based on thecomparison indicating the processing capabilities of the hardwareresources available to the first host instance are equal to theprocessing capabilities of the hardware resources available to thesecond host instance.

Various embodiments described herein may include a computer-programproduct tangibly embodied in a non-transitory machine-readable storagemedium, the computer-program product including instructions operable tocause a processor to perform operations comprising: implement one ormore service agents with each host instance in a cluster having aplurality of host instances, the one or more service agents to performtasks included in a workload; utilize a shared cache with the one ormore service agents of the plurality of hosts instances to automaticallybalance processing of the tasks in the workload among the host instancesin the cluster without a centralized controller or a load balancer;perform a task in the workload with a first service agent based on adata structure in the shared cache that indicates the first serviceagent is an active agent for the task, the first service agent of theone or more service agents implemented by a first host instance in thecluster of host instances; implement a second service agent of the oneor more service agents with a second host instance in the cluster ofhost instances, the second service agent to: transition from a sleepstate to an active state in response to expiration of a timer; identifya number of active service agents associated with the first hostinstance based on the data structure in the shared cache; compute adifference between the number of active service agents associated withthe first host instance and a number of active service agents associatedwith the second host instance; compare the difference between the numberof active service agents associated with the first host instance and thenumber of active service agents associated with the second host instanceto a load difference threshold; and update the data structure in theshared cache to indicate that the second service agent is the activeagent for the task to reduce a portion of the workload performed by thefirst service agent of the one or more service agents implemented by thefirst host instance in the cluster of host instances when the differencebetween the number of active service agents associated with the firsthost instance and the number of active service agents associated withthe second host instance exceeds the load difference threshold.

In one or more embodiments, the first host instance and the second hostinstance may have local access to a synchronized copy of the sharedcached.

In some embodiments, the second service agent may initialize a timer andtransition into the sleep state when the difference between the numberof active service agents associated with the first host instance and thenumber of active service agents associated with the second host instancedoes not exceed the load difference threshold.

In various embodiments, the second service agent performs a second taskin the workload and updates a timestamp in the shared cached to acurrent time based on performance of the second task.

In one or more embodiments, the data structure may include anassociation between the timestamp and a type of the task.

In some embodiments, the first service agent may comprise a metriccollecting agent and wherein the task includes a portion of a resourcemetric collection service that monitors dynamic metrics associated withhealth of a software system.

In various embodiments, the first service agent may update a timestampin the shared cached to indicate completion of the task.

In one or more embodiments, the first service agent may identify thetask for performance based on a task queue in the shared cache.

In some embodiments, the load difference threshold may be determinedbased on a comparison of processing capabilities of hardware resourcesavailable to the first host instance to processing capabilities ofhardware resources available to the second host instance.

In various embodiments, the load difference may be set to zero based onthe comparison indicating the processing capabilities of the hardwareresources available to the first host instance are equal to theprocessing capabilities of the hardware resources available to thesecond host instance.

The foregoing, together with other features and embodiments, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedfigures:

FIG. 1 illustrates a block diagram that provides an illustration of thehardware components of a computing system, according to some embodimentsof the present technology.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to some embodiments of the present technology.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to some embodiments of thepresent technology.

FIG. 4 illustrates a communications grid computing system including avariety of control and worker nodes, according to some embodiments ofthe present technology.

FIG. 5 illustrates a flow chart showing an example process for adjustinga communications grid or a work project in a communications grid after afailure of a node, according to some embodiments of the presenttechnology.

FIG. 6 illustrates a portion of a communications grid computing systemincluding a control node and a worker node, according to someembodiments of the present technology.

FIG. 7 illustrates a flow chart showing an example process for executinga data analysis or processing project, according to some embodiments ofthe present technology.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology.

FIG. 10 illustrates an ESP system interfacing between a publishingdevice and multiple event subscribing devices, according to embodimentsof the present technology.

FIG. 11A illustrates a flow chart showing an example process forgenerating and using a machine-learning model, according to someembodiments of the present technology.

FIG. 11B illustrates a neural network including multiple layers ofinterconnected neurons, according to some embodiments of the presenttechnology.

FIG. 12 illustrates an operating environment for an exemplary hostcluster, according to some embodiments of the present technology.

FIGS. 13A and 13B illustrate operating environments for exemplary sharedcaches, according to some embodiments of the present technology.

FIG. 14 illustrates an operating environment for exemplary serviceagents in a host cluster, according to some embodiments of the presenttechnology.

FIG. 15 illustrates an example of a first logic flow for a hostinstance, according to some embodiments of the present technology.

FIGS. 16A and 16B illustrate exemplary states of a host instance,according to some embodiments of the present technology.

FIGS. 17A-17C illustrate exemplary states of a host cluster, accordingto some embodiments of the present technology.

FIGS. 18A and 18B illustrate an example of a second logic flow for ahost instance, according to some embodiments of the present technology.

FIGS. 19A and 19B illustrates an example of a logic flow for a hostcluster, according to some embodiments of the present technology.

DETAILED DESCRIPTION

Various embodiments are generally directed to decentralized loadbalancing in a host cluster utilized to coordinate performance of tasksin a workload, such as via service agents and/or host instances includedin the host cluster, for instance. Some embodiments are particularlydirected to a set of service agents on one or more host instances thatutilize a shared cache to coordinate among themselves to balance aworkload with no centralized controller or balancer. In one or moreembodiments, a set of service agents may automatically balance aworkload among themselves using a shared cache. In one or more suchembodiments, the set of service agents may balance a workload amongthemselves by reading and/or writing to a data structure in the sharedcache. For example, the set of service agents may balance the workloadin response to one or more of a change in the number of host instancesor service agents, failure of a service agent, and excessive time forcompletion of a task. In some embodiments, the load, or task queue, ofeach host instance and/or service agent in a host cluster may bebalanced such that a busier component will release tasks to other lessbusy components. These and other embodiments are described and claimed.

Some challenges facing load balancing among components of a host clusterinclude the inability to balance a load without a centralized controlleror balancer to coordinate service agents (also referred to as agents) onthe host instances. This inability to balance a load without acentralized controller may result in the system having a single point offailure. For instance, all work may be received, distributed, and/orbalanced by a centralized controller. In such instances, the centralizedcontroller may be a single point of failure such that if the centralizedcontroller fails, the entire system fails. These challenges may resultin systems with reduced availability and limited adaptability. Addingfurther complexity, reliable execution of a variety of types of taskswith different processing requirements may be critical. For example, thetasks may be associated with monitoring the health of a software system.In some embodiments, performance tasks in a workload may be slow orunresponsive due to limited availability of service agents, resulting inpoor utilization of processing capabilities of a system. Suchlimitations can drastically reduce the usability, applicability, andperformance of host clusters, contributing to inefficient systems withlimited flexibility and poor reliability.

Various embodiments described herein include a cluster of host instances(also referred to as service instances) that coordinate among themselveswithout a centralized controller or balancer. In one or moreembodiments, this may enable the host cluster to achieve highavailability without a single point of failure. In some embodiments,service agents running inside the host instances may automaticallybalance a workload among themselves using a shared cache. In some suchembodiments, the service agents running inside the host instances mayautomatically balance the workload when there is a change in the numberof host instances or service agents, when a service agent fails, or whena service agent takes too long to complete a task. In many embodiments,service agents may include one or more metric collecting agents tomonitor metrics associated with health of a software system. In manysuch embodiments, the metrics may include dynamic system attributes suchas central processing unit (CPU) or memory usages. In one or moreembodiments, data collected by service agents may be organized and/orpresented to a user. In various embodiments, one or more data structuresmay be generated, maintained, and/or updated in a shared cache byservice agents to coordinate distribution of a workload. In various suchembodiments, the data structures may indicate one or more parametersassociated with distribution and/or execution of tasks in the workload.

In one or more embodiments, the number and availability of serviceagents may vary based on workload demands. For example, service agentsmay periodically determine whether to begin to actively perform tasks ina workload based on data stored in a shared cache associated withperformance of tasks in a workload. In such instances, the data mayinclude a timestamp associated with performance of a task in a workload.In some embodiments, service agents may periodically determine whetherto begin to actively perform tasks in a workload (e.g., transition froma sleep state to an active state) based on data stored in a shared cacheassociated with performance of tasks in a workload. For example, if aperiod of time elapsed since a timestamp in a shared cached exceeds aservice threshold, a service agent may determine to transition from asleep state to an active state.

In various embodiments, the number of active service agents (alsoreferred to as active agents) on each host instance may be balanced. Forinstance, if one host instance implements two active agents and anotherhost instance implements no active agents, the system may be rebalancedsuch that each host instance has one active agent. In many embodiments,one or more agents, host instances, or task types may be weighteddifferently when balancing a workload. In some embodiments, distributionof tasks and/or resources among host instances may be dynamicallycontrolled. In some such embodiments, the amount of each type of serviceagents implemented by or the portions of tasks assigned to each hostinstance may be determined based on weighting conditions.

In many embodiments, the weighting conditions may include a weightassociated with or assigned to each host instance in a cluster. In oneor more embodiments, a weight may indicate a rating of resourcesavailable to a host instance. In one or more such embodiments, therating may be relative to other host instances in a host cluster. Invarious embodiments, the weighting conditions may be based on processingcapabilities of hardware resources available to respective hostinstances. In various such embodiments, this may enable betterutilization of host instances in a cluster. For instance, if a firsthost instance has a greater weight than a second host instance, aworkload may be balanced such that the first host instance, or serviceagent(s) implemented thereon, receive a larger portion than the secondhost instance, or service agent(s) implemented thereon. In someinstances, more service agents may be implemented on a host instancewith a greater weight than host instances with less weight. Similarly,in various embodiments, requirements of different tasks or theirfrequency may be factored into how a workload is distributed.

In one or more embodiments, different resources may be provisioned tovarious service agents. In some embodiments, capabilities of differenthost instances, such as the capabilities of underlying hardware, may beutilized to determine a weight associated with a respective hostinstance. In some such instances, the capabilities of different hostinstances may thereby be factored into how a workload is distributed.For instance, if a first host instance has greater processingcapabilities than a second host instance, a workload may be balancedsuch that the first host instance, or service agent(s) implementedthereon, receive a larger portion than the second host instance, orservice agent(s) implemented thereon.

In these and other ways, the host cluster may enable customized,efficient, and reliable distribution of workloads to provide a systemwith improved capacity and increased availability, resulting in severaltechnical effects and advantages. In various embodiments, the componentsof the host cluster may be implemented via one or more computingdevices, and thereby provide additional and useful functionality to theone or more computing devices, resulting in more capable and betterfunctioning computing devices. For example, the host cluster may be ableto distribute workloads in a decentralized manner, resulting in a taskexecution without a single point of failure.

With general reference to notations and nomenclature used herein,portions of the detailed description that follows may be presented interms of program procedures executed by a processor of a machine or ofmultiple networked machines. These procedural descriptions andrepresentations are used by those skilled in the art to most effectivelyconvey the substance of their work to others skilled in the art. Aprocedure is here, and generally, conceived to be a self-consistentsequence of operations leading to a desired result. These operations arethose requiring physical manipulations of physical quantities. Usually,though not necessarily, these quantities take the form of electrical,magnetic or optical communications capable of being stored, transferred,combined, compared, and otherwise manipulated. It proves convenient attimes, principally for reasons of common usage, to refer to what iscommunicated as bits, values, elements, symbols, characters, terms,numbers, or the like. It should be noted, however, that all of these andsimilar terms are to be associated with the appropriate physicalquantities and are merely convenient labels applied to those quantities.

Further, these manipulations are often referred to in terms, such asadding or comparing, which are commonly associated with mentaloperations performed by a human operator. However, no such capability ofa human operator is necessary, or desirable in most cases, in any of theoperations described herein that form part of one or more embodiments.Rather, these operations are machine operations. Useful machines forperforming operations of various embodiments include machinesselectively activated or configured by a routine stored within that iswritten in accordance with the teachings herein, and/or includeapparatus specially constructed for the required purpose. Variousembodiments also relate to apparatus or systems for performing theseoperations. These apparatuses may be specially constructed for therequired purpose or may include a general-purpose computer. The requiredstructure for a variety of these machines will appear from thedescription given.

Reference is now made to the drawings, wherein like reference numeralsare used to refer to like elements throughout. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding thereof. It maybe evident, however, that the novel embodiments can be practiced withoutthese specific details. In other instances, well known structures anddevices are shown in block diagram form in order to facilitate adescription thereof. The intention is to cover all modifications,equivalents, and alternatives within the scope of the claims.

Systems depicted in some of the figures may be provided in variousconfigurations. In some embodiments, the systems may be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing systemand/or a fog computing system.

FIG. 1 is a block diagram that provides an illustration of the hardwarecomponents of a data transmission network 100, according to embodimentsof the present technology. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. Data transmission network 100 also includes one or morenetwork devices 102. Network devices 102 may include client devices thatattempt to communicate with computing environment 114. For example,network devices 102 may send data to the computing environment 114 to beprocessed, may send signals to the computing environment 114 to controldifferent aspects of the computing environment or the data it isprocessing, among other reasons. Network devices 102 may interact withthe computing environment 114 through a number of ways, such as, forexample, over one or more networks 108. As shown in FIG. 1, computingenvironment 114 may include one or more other systems. For example,computing environment 114 may include a database system 118 and/or acommunications grid 120.

In other embodiments, network devices may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP), described further with respect to FIGS.8-10), to the computing environment 114 via networks 108. For example,network devices 102 may include network computers, sensors, databases,or other devices that may transmit or otherwise provide data tocomputing environment 114. For example, network devices may includelocal area network devices, such as routers, hubs, switches, or othercomputer networking devices. These devices may provide a variety ofstored or generated data, such as network data or data specific to thenetwork devices themselves. Network devices may also include sensorsthat monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devicesmay provide data they collect over time. Network devices may alsoinclude devices within the internet of things, such as devices within ahome automation network. Some of these devices may be referred to asedge devices, and may involve edge computing circuitry. Data may betransmitted by network devices directly to computing environment 114 orto network-attached data stores, such as network-attached data stores110 for storage so that the data may be retrieved later by the computingenvironment 114 or other portions of data transmission network 100.

Data transmission network 100 may also include one or morenetwork-attached data stores 110. Network-attached data stores 110 areused to store data to be processed by the computing environment 114 aswell as any intermediate or final data generated by the computing systemin non-volatile memory. However, in certain embodiments, theconfiguration of the computing environment 114 allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory (e.g., disk). This can be useful in certain situations, such aswhen the computing environment 114 receives ad hoc queries from a userand when responses, which are generated by processing large amounts ofdata, need to be generated on-the-fly. In this non-limiting situation,the computing environment 114 may be configured to retain the processedinformation within memory so that responses can be generated for theuser at different levels of detail as well as allow a user tointeractively query against this information.

Network-attached data stores may store a variety of different types ofdata organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data storage mayinclude storage other than primary storage located within computingenvironment 114 that is directly accessible by processors locatedtherein. Network-attached data storage may include secondary, tertiaryor auxiliary storage, such as large hard drives, servers, virtualmemory, among other types. Storage devices may include portable ornon-portable storage devices, optical storage devices, and various othermediums capable of storing, containing data. A machine-readable storagemedium or computer-readable storage medium may include a non-transitorymedium in which data can be stored and that does not include carrierwaves and/or transitory electronic signals. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode and/or machine-executable instructions that may represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, amongothers. Furthermore, the data stores may hold a variety of differenttypes of data. For example, network-attached data stores 110 may holdunstructured (e.g., raw) data, such as manufacturing data (e.g., adatabase containing records identifying products being manufactured withparameter data for each product, such as colors and models) or productsales databases (e.g., a database containing individual data recordsidentifying details of individual product sales).

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data and/or structured hierarchically according toone or more dimensions (e.g., parameters, attributes, and/or variables).For example, data may be stored in a hierarchical data structure, suchas a ROLAP OR MOLAP database, or may be stored in another tabular form,such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the one or more sever farms 106 or one or more servers within theserver farms. Server farms 106 can be configured to provide informationin a predetermined manner. For example, server farms 106 may access datato transmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, and/or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or moresensors, as inputs from a control database, or may have been received asinputs from an external system or device. Server farms 106 may assist inprocessing the data by turning raw data into processed data based on oneor more rules implemented by the server farms. For example, sensor datamay be analyzed to determine changes in an environment over time or inreal-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain embodiments, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1. Services provided by the cloudnetwork can dynamically scale to meet the needs of its users. The cloudnetwork 116 may comprise one or more computers, servers, and/or systems.In some embodiments, the computers, servers, and/or systems that make upthe cloud network 116 are different from the user's own on-premisescomputers, servers, and/or systems. For example, the cloud network 116may host an application, and a user may, via a communication networksuch as the Internet, on demand, order and use the application.

While each device, server and system in FIG. 1 is shown as a singledevice, it will be appreciated that multiple devices may instead beused. For example, a set of network devices can be used to transmitvarious communications from a single user, or remote server 140 mayinclude a server stack. As another example, data may be processed aspart of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between servers 106 and computing environment 114 orbetween a server and a device) may occur over one or more networks 108.Networks 108 may include one or more of a variety of different types ofnetworks, including a wireless network, a wired network, or acombination of a wired and wireless network. Examples of suitablenetworks include the Internet, a personal area network, a local areanetwork (LAN), a wide area network (WAN), or a wireless local areanetwork (WLAN). A wireless network may include a wireless interface orcombination of wireless interfaces. As an example, a network in the oneor more networks 108 may include a short-range communication channel,such as a Bluetooth or a Bluetooth Low Energy channel. A wired networkmay include a wired interface. The wired and/or wireless networks may beimplemented using routers, access points, bridges, gateways, or thelike, to connect devices in the network 114, as will be furtherdescribed with respect to FIG. 2. The one or more networks 108 can beincorporated entirely within or can include an intranet, an extranet, ora combination thereof. In one embodiment, communications between two ormore systems and/or devices can be achieved by a secure communicationsprotocol, such as secure sockets layer (SSL) or transport layer security(TLS). In addition, data and/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings and/or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics. This will be described further below with respectto FIG. 2.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The compute nodes in the grid-basedcomputing system 120 and the transmission network database system 118may share the same processor hardware, such as processors that arelocated within computing environment 114.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to embodiments of the present technology. As noted,each communication within data transmission network 100 may occur overone or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. For example,network device 204 may collect data either from its surroundingenvironment or from other network devices (such as network devices205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, electrical current, among others.The sensors may be mounted to various components used as part of avariety of different types of systems (e.g., an oil drilling operation).The network devices may detect and record data related to theenvironment that it monitors, and transmit that data to computingenvironment 214.

As noted, one type of system that may include various sensors thatcollect data to be processed and/or transmitted to a computingenvironment according to certain embodiments includes an oil drillingsystem. For example, the one or more drilling operation sensors mayinclude surface sensors that measure a hook load, a fluid rate, atemperature and a density in and out of the wellbore, a standpipepressure, a surface torque, a rotation speed of a drill pipe, a rate ofpenetration, a mechanical specific energy, etc. and downhole sensorsthat measure a rotation speed of a bit, fluid densities, downholetorque, downhole vibration (axial, tangential, lateral), a weightapplied at a drill bit, an annular pressure, a differential pressure, anazimuth, an inclination, a dog leg severity, a measured depth, avertical depth, a downhole temperature, etc. Besides the raw datacollected directly by the sensors, other data may include parameterseither developed by the sensors or assigned to the system by a client orother controlling device. For example, one or more drilling operationcontrol parameters may control settings such as a mud motor speed toflow ratio, a bit diameter, a predicted formation top, seismic data,weather data, etc. Other data may be generated using physical modelssuch as an earth model, a weather model, a seismic model, a bottom holeassembly model, a well plan model, an annular friction model, etc. Inaddition to sensor and control settings, predicted outputs, of forexample, the rate of penetration, mechanical specific energy, hook load,flow in fluid rate, flow out fluid rate, pump pressure, surface torque,rotation speed of the drill pipe, annular pressure, annular frictionpressure, annular temperature, equivalent circulating density, etc. mayalso be stored in the data warehouse.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a homeautomation or similar automated network in a different environment, suchas an office space, school, public space, sports venue, or a variety ofother locations. Network devices in such an automated network mayinclude network devices that allow a user to access, control, and/orconfigure various home appliances located within the user's home (e.g.,a television, radio, light, fan, humidifier, sensor, microwave, iron,and/or the like), or outside of the user's home (e.g., exterior motionsensors, exterior lighting, garage door openers, sprinkler systems, orthe like). For example, network device 102 may include a home automationswitch that may be coupled with a home appliance. In another embodiment,a network device can allow a user to access, control, and/or configuredevices, such as office-related devices (e.g., copy machine, printer, orfax machine), audio and/or video related devices (e.g., a receiver, aspeaker, a projector, a DVD player, or a television), media-playbackdevices (e.g., a compact disc player, a CD player, or the like),computing devices (e.g., a home computer, a laptop computer, a tablet, apersonal digital assistant (PDA), a computing device, or a wearabledevice), lighting devices (e.g., a lamp or recessed lighting), devicesassociated with a security system, devices associated with an alarmsystem, devices that can be operated in an automobile (e.g., radiodevices, navigation devices), and/or the like. Data may be collectedfrom such various sensors in raw form, or data may be processed by thesensors to create parameters or other data either developed by thesensors based on the raw data or assigned to the system by a client orother controlling device.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a poweror energy grid. A variety of different network devices may be includedin an energy grid, such as various devices within one or more powerplants, energy farms (e.g., wind farm, solar farm, among others) energystorage facilities, factories, homes and businesses of consumers, amongothers. One or more of such devices may include one or more sensors thatdetect energy gain or loss, electrical input or output or loss, and avariety of other efficiencies. These sensors may collect data to informusers of how the energy grid, and individual devices within the grid,may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data it collectsbefore transmitting the data to the computing environment 114, or beforedeciding whether to transmit data to the computing environment 114. Forexample, network devices may determine whether data collected meetscertain rules, for example by comparing data or values computed from thedata and comparing that data to one or more thresholds. The networkdevice may use this data and/or comparisons to determine if the datashould be transmitted to the computing environment 214 for further useor processing.

Computing environment 214 may include machines 220 and 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines, 220and 240, computing environment 214 may have only one machine or may havemore than two machines. The machines that make up computing environment214 may include specialized computers, servers, or other machines thatare configured to individually and/or collectively process large amountsof data. The computing environment 214 may also include storage devicesthat include one or more databases of structured data, such as dataorganized in one or more hierarchies, or unstructured data. Thedatabases may communicate with the processing devices within computingenvironment 214 to distribute data to them. Since network devices maytransmit data to computing environment 214, that data may be received bythe computing environment 214 and subsequently stored within thosestorage devices. Data used by computing environment 214 may also bestored in data stores 235, which may also be a part of or connected tocomputing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withdevices 230 via one or more routers 225. Computing environment 214 maycollect, analyze and/or store data from or pertaining to communications,client device operations, client rules, and/or user-associated actionsstored at one or more data stores 235. Such data may influencecommunication routing to the devices within computing environment 214,how data is stored or processed within computing environment 214, amongother actions.

Notably, various other devices can further be used to influencecommunication routing and/or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include aweb server 240. Thus, computing environment 214 can retrieve data ofinterest, such as client information (e.g., product information, clientrules, etc.), technical product details, news, current or predictedweather, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices may receive data periodically from network device sensors as thesensors continuously sense, monitor and track changes in theirenvironments. Devices within computing environment 214 may also performpre-analysis on data it receives to determine if the data receivedshould be processed as part of an ongoing project. The data received andcollected by computing environment 214, no matter what the source ormethod or timing of receipt, may be processed over a period of time fora client to determine results data based on the client's needs andrules.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to embodiments of the presenttechnology. More specifically, FIG. 3 identifies operation of acomputing environment in an Open Systems Interaction model thatcorresponds to various connection components. The model 300 shows, forexample, how a computing environment, such as computing environment 314(or computing environment 214 in FIG. 2) may communicate with otherdevices in its network, and control how communications between thecomputing environment and other devices are executed and under whatconditions.

The model can include layers 302-314. The layers are arranged in astack. Each layer in the stack serves the layer one level higher than it(except for the application layer, which is the highest layer), and isserved by the layer one level below it (except for the physical layer,which is the lowest layer). The physical layer is the lowest layerbecause it receives and transmits raw bites of data, and is the farthestlayer from the user in a communications system. On the other hand, theapplication layer is the highest layer because it interacts directlywith a software application.

As noted, the model includes a physical layer 302. Physical layer 302represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagnetic signals.Physical layer 302 also defines protocols that may controlcommunications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (i.e.,move) data across a network. The link layer manages node-to-nodecommunications, such as within a grid computing environment. Link layer304 can detect and correct errors (e.g., transmission errors in thephysical layer 302). Link layer 304 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 306 defines the protocol for routing within a network. Inother words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid computing environment).Network layer 306 can also define the processes used to structure localaddressing within the network.

Transport layer 308 can manage the transmission of data and the qualityof the transmission and/or receipt of that data. Transport layer 308 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 308 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt and/or format data based on data types and/orencodings known to be accepted by an application or network layer.

Application layer 314 interacts directly with software applications andend users, and manages communications between them. Application layer314 can identify destinations, local resource states or availabilityand/or communication content or formatting using the applications.

Intra-network connection components 322 and 324 are shown to operate inlower levels, such as physical layer 302 and link layer 304,respectively. For example, a hub can operate in the physical layer, aswitch can operate in the physical layer, and a router can operate inthe network layer. Inter-network connection components 326 and 328 areshown to operate on higher levels, such as layers 306-314. For example,routers can operate in the network layer and network devices can operatein the transport, session, presentation, and application layers.

As noted, a computing environment 314 can interact with and/or operateon, in various embodiments, one, more, all or any of the various layers.For example, computing environment 314 can interact with a hub (e.g.,via the link layer) so as to adjust which devices the hub communicateswith. The physical layer may be served by the link layer, so it mayimplement such data from the link layer. For example, the computingenvironment 314 may control which devices it will receive data from. Forexample, if the computing environment 314 knows that a certain networkdevice has turned off, broken, or otherwise become unavailable orunreliable, the computing environment 314 may instruct the hub toprevent any data from being transmitted to the computing environment 314from that network device. Such a process may be beneficial to avoidreceiving data that is inaccurate or that has been influenced by anuncontrolled environment. As another example, computing environment 314can communicate with a bridge, switch, router or gateway and influencewhich device within the system (e.g., system 200) the component selectsas a destination. In some embodiments, computing environment 314 caninteract with various layers by exchanging communications with equipmentoperating on a particular layer by routing or modifying existingcommunications. In another embodiment, such as in a grid computingenvironment, a node may determine how data within the environment shouldbe routed (e.g., which node should receive certain data) based oncertain parameters or information provided by other layers within themodel.

As noted, the computing environment 314 may be a part of acommunications grid environment, the communications of which may beimplemented as shown in the protocol of FIG. 3. For example, referringto FIG. 2, one or more of machines 220 and 240 may be part of acommunications grid computing environment. A gridded computingenvironment may be employed in a distributed system with non-interactiveworkloads where data resides in memory on the machines, or computenodes. In such an environment, analytic code, instead of a databasemanagement system, controls the processing performed by the nodes. Datais co-located by pre-distributing it to the grid nodes, and the analyticcode on each node loads the local data into memory. Each node may beassigned a particular task such as a portion of a processing project, orto organize or control other nodes within the grid.

FIG. 4 illustrates a communications grid computing system 400 includinga variety of control and worker nodes, according to embodiments of thepresent technology. Communications grid computing system 400 includesthree control nodes and one or more worker nodes. Communications gridcomputing system 400 includes control nodes 402, 404, and 406. Thecontrol nodes are communicatively connected via communication paths 451,453, and 455. Therefore, the control nodes may transmit information(e.g., related to the communications grid or notifications), to andreceive information from each other. Although communications gridcomputing system 400 is shown in FIG. 4 as including three controlnodes, the communications grid may include more or less than threecontrol nodes.

Communications grid computing system (or just “communications grid”) 400also includes one or more worker nodes. Shown in FIG. 4 are six workernodes 410-420. Although FIG. 4 shows six worker nodes, a communicationsgrid according to embodiments of the present technology may include moreor less than six worker nodes. The number of worker nodes included in acommunications grid may be dependent upon how large the project or dataset is being processed by the communications grid, the capacity of eachworker node, the time designated for the communications grid to completethe project, among others. Each worker node within the communicationsgrid 400 may be connected (wired or wirelessly, and directly orindirectly) to control nodes 402-406. Therefore, each worker node mayreceive information from the control nodes (e.g., an instruction toperform work on a project) and may transmit information to the controlnodes (e.g., a result from work performed on a project). Furthermore,worker nodes may communicate with each other (either directly orindirectly). For example, worker nodes may transmit data between eachother related to a job being performed or an individual task within ajob being performed by that worker node. However, in certainembodiments, worker nodes may not, for example, be connected(communicatively or otherwise) to certain other worker nodes. In anembodiment, worker nodes may only be able to communicate with thecontrol node that controls it, and may not be able to communicate withother worker nodes in the communications grid, whether they are otherworker nodes controlled by the control node that controls the workernode, or worker nodes that are controlled by other control nodes in thecommunications grid.

A control node may connect with an external device with which thecontrol node may communicate (e.g., a grid user, such as a server orcomputer, may connect to a controller of the grid). For example, aserver or computer may connect to control nodes and may transmit aproject or job to the node. The project may include a data set. The dataset may be of any size. Once the control node receives such a projectincluding a large data set, the control node may distribute the data setor projects related to the data set to be performed by worker nodes.Alternatively, for a project including a large data set, the data setmay be received or stored by a machine other than a control node (e.g.,a Hadoop data node employing Hadoop Distributed File System, or HDFS).

Control nodes may maintain knowledge of the status of the nodes in thegrid (i.e., grid status information), accept work requests from clients,subdivide the work across worker nodes, coordinate the worker nodes,among other responsibilities. Worker nodes may accept work requests froma control node and provide the control node with results of the workperformed by the worker node. A grid may be started from a single node(e.g., a machine, computer, server, etc.). This first node may beassigned or may start as the primary control node that will control anyadditional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (i.e., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node receives aproject, the primary control node may distribute portions of the projectto its worker nodes for execution. For example, when a project isinitiated on communications grid 400, primary control node 402 controlsthe work to be performed for the project in order to complete theproject as requested or instructed. The primary control node maydistribute work to the worker nodes based on various factors, such aswhich subsets or portions of projects may be completed most efficientlyand in the correct amount of time. For example, a worker node mayperform analysis on a portion of data that is already local (e.g.,stored on) the worker node. The primary control node also coordinatesand processes the results of the work performed by each worker nodeafter each worker node executes and completes its job. For example, theprimary control node may receive a result from one or more worker nodes,and the control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404 and 406, may beassigned as backup control nodes for the project. In an embodiment,backup control nodes may not control any portion of the project.Instead, backup control nodes may serve as a backup for the primarycontrol node and take over as primary control node if the primarycontrol node were to fail. If a communications grid were to include onlya single control node, and the control node were to fail (e.g., thecontrol node is shut off or breaks) then the communications grid as awhole may fail and any project or job being run on the communicationsgrid may fail and may not complete. While the project may be run again,such a failure may cause a delay (severe delay in some cases, such asovernight delay) in completion of the project. Therefore, a grid withmultiple control nodes, including a backup control node, may bebeneficial.

To add another node or machine to the grid, the primary control node mayopen a pair of listening sockets, for example. A socket may be used toaccept work requests from clients, and the second socket may be used toaccept connections from other grid nodes. The primary control node maybe provided with a list of other nodes (e.g., other machines, computers,servers) that will participate in the grid, and the role that each nodewill fill in the grid. Upon startup of the primary control node (e.g.,the first node on the grid), the primary control node may use a networkprotocol to start the server process on every other node in the grid.Command line parameters, for example, may inform each node of one ormore pieces of information, such as: the role that the node will have inthe grid, the host name of the primary control node, the port number onwhich the primary control node is accepting connections from peer nodes,among others. The information may also be provided in a configurationfile, transmitted over a secure shell tunnel, recovered from aconfiguration server, among others. While the other machines in the gridmay not initially know about the configuration of the grid, thatinformation may also be sent to each other node by the primary controlnode. Updates of the grid information may also be subsequently sent tothose nodes.

For any control node, other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it will check to see if italready has a connection to that other node. If it does not have aconnection to that node, it may then establish a connection to thatcontrol node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. However, a hierarchy of nodes may also bedetermined using methods other than using the unique identifiers of thenodes. For example, the hierarchy may be predetermined, or may beassigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404 and 406 (and, for example, toother control or worker nodes within the communications grid). Suchcommunications may be sent periodically, at fixed time intervals,between known fixed stages of the project's execution, among otherprotocols. The communications transmitted by primary control node 402may be of varied types and may include a variety of types ofinformation. For example, primary control node 402 may transmitsnapshots (e.g., status information) of the communications grid so thatbackup control node 404 always has a recent snapshot of thecommunications grid. The snapshot or grid status may include, forexample, the structure of the grid (including, for example, the workernodes in the grid, unique identifiers of the nodes, or theirrelationships with the primary control node) and the status of a project(including, for example, the status of each worker node's portion of theproject). The snapshot may also include analysis or results receivedfrom worker nodes in the communications grid. The backup control nodesmay receive and store the backup data received from the primary controlnode. The backup control nodes may transmit a request for such asnapshot (or other information) from the primary control node, or theprimary control node may send such information periodically to thebackup control nodes.

As noted, the backup data may allow the backup control node to take overas primary control node if the primary control node fails withoutrequiring the grid to start the project over from scratch. If theprimary control node fails, the backup control node that will take overas primary control node may retrieve the most recent version of thesnapshot received from the primary control node and use the snapshot tocontinue the project from the stage of the project indicated by thebackup data. This may prevent failure of the project as a whole.

A backup control node may use various methods to determine that theprimary control node has failed. In one example of such a method, theprimary control node may transmit (e.g., periodically) a communicationto the backup control node that indicates that the primary control nodeis working and has not failed, such as a heartbeat communication. Thebackup control node may determine that the primary control node hasfailed if the backup control node has not received a heartbeatcommunication for a certain predetermined period of time. Alternatively,a backup control node may also receive a communication from the primarycontrol node itself (before it failed) or from a worker node that theprimary control node has failed, for example because the primary controlnode has failed to communicate with the worker node.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404and 406) will take over for failed primary control node 402 and becomethe new primary control node. For example, the new primary control nodemay be chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative embodiment, abackup control node may be assigned to be the new primary control nodeby another device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeembodiment, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeembodiment, the primary control node may transmit a communication toeach of the operable worker nodes still on the communications grid thateach of the worker nodes should purposefully fail also. After each ofthe worker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed.

FIG. 5 illustrates a flow chart showing an example process for adjustinga communications grid or a work project in a communications grid after afailure of a node, according to embodiments of the present technology.The process may include, for example, receiving grid status informationincluding a project status of a portion of a project being executed by anode in the communications grid, as described in operation 502. Forexample, a control node (e.g., a backup control node connected to aprimary control node and a worker node on a communications grid) mayreceive grid status information, where the grid status informationincludes a project status of the primary control node or a projectstatus of the worker node. The project status of the primary controlnode and the project status of the worker node may include a status ofone or more portions of a project being executed by the primary andworker nodes in the communications grid. The process may also includestoring the grid status information, as described in operation 504. Forexample, a control node (e.g., a backup control node) may store thereceived grid status information locally within the control node.Alternatively, the grid status information may be sent to another devicefor storage where the control node may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 illustrates a portion of a communications grid computing system600 including a control node and a worker node, according to embodimentsof the present technology. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viapath 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 comprise multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes a database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However, in certain embodiments, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DMBS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 620 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 620 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.The GESC at the control node 602 can communicate, over a communicationpath 652, with a client deice 630. More specifically, control node 602may communicate with client application 632 hosted by the client device630 to receive queries and to respond to those queries after processinglarge amounts of data.

DMBS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within a node 602 or 610. The database mayorganize data stored in data stores 624. The DMBS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 illustrates a flow chart showing an example method for executinga project within a grid computing system, according to embodiments ofthe present technology. As described with respect to FIG. 6, the GESC atthe control node may transmit data with a client device (e.g., clientdevice 630) to receive queries for executing a project and to respond tothose queries after large amounts of data have been processed. The querymay be transmitted to the control node, where the query may include arequest for executing a project, as described in operation 702. Thequery can contain instructions on the type of data analysis to beperformed in the project and whether the project should be executedusing the grid-based computing environment, as shown in operation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 874 a-c,described further with respect to FIG. 10, may also subscribe to theESPE. The ESPE may determine or define how input data or event streamsfrom network devices or other publishers (e.g., network devices 204-209in FIG. 2) are transformed into meaningful output data to be consumed bysubscribers, such as for example client devices 230 in FIG. 2.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology. ESPE 800 may include one or more projects 802. A project maybe described as a second-level container in an engine model managed byESPE 800 where a thread pool size for the project may be defined by auser. Each project of the one or more projects 802 may include one ormore continuous queries 804 that contain data flows, which are datatransformations of incoming event streams. The one or more continuousqueries 804 may include one or more source windows 806 and one or morederived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeembodiment, for example, there may be only one ESPE 800 for eachinstance of the ESP application, and ESPE 800 may have a unique enginename. Additionally, the one or more projects 802 may each have uniqueproject names, and each query may have a unique continuous query nameand begin with a uniquely named source window of the one or more sourcewindows 806. ESPE 800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology. As noted, the ESPE 800 (oran associated ESP application) defines how input event streams aretransformed into meaningful output event streams. More specifically, theESP application may define how input event streams from publishers(e.g., network devices providing sensed data) are transformed intomeaningful output event streams consumed by subscribers (e.g., a dataanalytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop-downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. As furtherunderstood by a person of skill in the art, various operations may beperformed in parallel, for example, using a plurality of threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 illustrates an ESP system 850 interfacing between publishingdevice 872 and event subscribing devices 874 a-c, according toembodiments of the present technology. ESP system 850 may include ESPdevice or subsystem 851, event publishing device 872, an eventsubscribing device A 874 a, an event subscribing device B 874 b, and anevent subscribing device C 874 c. Input event streams are output to ESPdevice 851 by publishing device 872. In alternative embodiments, theinput event streams may be created by a plurality of publishing devices.The plurality of publishing devices further may publish event streams toother ESP devices. The one or more continuous queries instantiated byESPE 800 may analyze and process the input event streams to form outputevent streams output to event subscribing device A 874 a, eventsubscribing device B 874 b, and event subscribing device C 874 c. ESPsystem 850 may include a greater or a fewer number of event subscribingdevices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 872, to publish event streamsinto ESPE 800 or an event subscriber, such as event subscribing device A874 a, event subscribing device B 874 b, and event subscribing device C874 c, to subscribe to event streams from ESPE 800. For illustration,one or more publish/subscribe APIs may be defined. Using thepublish/subscribe API, an event publishing application may publish eventstreams into a running event stream processor project source window ofESPE 800, and the event subscription application may subscribe to anevent stream processor project source window of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 872, and event subscription applications instantiatedat one or more of event subscribing device A 874 a, event subscribingdevice B 874 b, and event subscribing device C 874 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of the eventpublishing device 872.

ESP subsystem 800 may include a publishing client 852, ESPE 800, asubscribing client A 854, a subscribing client B 856, and a subscribingclient C 858. Publishing client 852 may be started by an eventpublishing application executing at publishing device 872 using thepublish/subscribe API. Subscribing client A 854 may be started by anevent subscription application A, executing at event subscribing deviceA 874 a using the publish/subscribe API. Subscribing client B 856 may bestarted by an event subscription application B executing at eventsubscribing device B 874 b using the publish/subscribe API. Subscribingclient C 858 may be started by an event subscription application Cexecuting at event subscribing device C 874 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on event publishing device872. The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 852. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 854, subscribingclient B 806, and subscribing client C 808 and to event subscriptiondevice A 874 a, event subscription device B 874 b, and eventsubscription device C 874 c. Publishing client 852 may further generateand include a unique embedded transaction ID in the event block objectas the event block object is processed by a continuous query, as well asthe unique ID that publishing device 872 assigned to the event blockobject.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 874 a-c. For example, subscribing client A 804,subscribing client B 806, and subscribing client C 808 may send thereceived event block object to event subscription device A 874 a, eventsubscription device B 874 b, and event subscription device C 874 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 872,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some embodiments, big data is processed for an analyticsproject after the data is received and stored. In other embodiments,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the current disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations such asthose in support of an ongoing manufacturing or drilling operation. Anembodiment of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, aprocessor and a computer-readable medium operably coupled to theprocessor. The processor is configured to execute an ESP engine (ESPE).The computer-readable medium has instructions stored thereon that, whenexecuted by the processor, cause the computing device to support thefailover. An event block object is received from the ESPE that includesa unique identifier. A first status of the computing device as active orstandby is determined. When the first status is active, a second statusof the computing device as newly active or not newly active isdetermined. Newly active is determined when the computing device isswitched from a standby status to an active status. When the secondstatus is newly active, a last published event block object identifierthat uniquely identifies a last published event block object isdetermined. A next event block object is selected from a non-transitorycomputer-readable medium accessible by the computing device. The nextevent block object has an event block object identifier that is greaterthan the determined last published event block object identifier. Theselected next event block object is published to an out-messagingnetwork device. When the second status of the computing device is notnewly active, the received event block object is published to theout-messaging network device. When the first status of the computingdevice is standby, the received event block object is stored in thenon-transitory computer-readable medium.

FIG. 11A is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as Naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and managingmachine-learning models can include SAS® Enterprise Miner, SAS® RapidPredictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services(CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, N.C.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11A.

In block 1104, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1106, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1108, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, the machine-learning model may have ahigh degree of accuracy. Otherwise, the machine-learning model may havea low degree of accuracy. The 90% number is an example only. A realisticand desirable accuracy percentage is dependent on the problem and thedata.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 1106,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1150 shown in FIG. 11B. The neural network 1150 is representedas multiple layers of interconnected neurons, such as neuron 1158, thatcan exchange data between one another. The layers include an input layer1152 for receiving input data, a hidden layer 1154, and an output layer1156 for providing a result. The hidden layer 1154 is referred to ashidden because it may not be directly observable or have its inputdirectly accessible during the normal functioning of the neural network1150. Although the neural network 1150 is shown as having a specificnumber of layers and neurons for exemplary purposes, the neural network1150 can have any number and combination of layers, and each layer canhave any number and combination of neurons.

The neurons and connections between the neurons can have numericweights, which can be tuned during training. For example, training datacan be provided to the input layer 1152 of the neural network 1150, andthe neural network 1150 can use the training data to tune one or morenumeric weights of the neural network 1150. In some examples, the neuralnetwork 1150 can be trained using backpropagation. Backpropagation caninclude determining a gradient of a particular numeric weight based on adifference between an actual output of the neural network 1150 and adesired output of the neural network 1150. Based on the gradient, one ormore numeric weights of the neural network 1150 can be updated to reducethe difference, thereby increasing the accuracy of the neural network1150. This process can be repeated multiple times to train the neuralnetwork 1150. For example, this process can be repeated hundreds orthousands of times to train the neural network 1150.

In some examples, the neural network 1150 is a feed-forward neuralnetwork. In a feed-forward neural network, every neuron only propagatesan output value to a subsequent layer of the neural network 1150. Forexample, data may only move one direction (forward) from one neuron tothe next neuron in a feed-forward neural network.

In other examples, the neural network 1150 is a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops, allowing data to propagate in both forward and backward throughthe neural network 1150. This can allow for information to persistwithin the recurrent neural network. For example, a recurrent neuralnetwork can determine an output based at least partially on informationthat the recurrent neural network has seen before, giving the recurrentneural network the ability to use previous input to inform the output.

In some examples, the neural network 1150 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer ofthe neural network 1150. Each subsequent layer of the neural network1150 can repeat this process until the neural network 1150 outputs afinal result at the output layer 1156. For example, the neural network1150 can receive a vector of numbers as an input at the input layer1152. The neural network 1150 can multiply the vector of numbers by amatrix of numeric weights to determine a weighted vector. The matrix ofnumeric weights can be tuned during the training of the neural network1150. The neural network 1150 can transform the weighted vector using anonlinearity, such as a sigmoid tangent or the hyperbolic tangent. Insome examples, the nonlinearity can include a rectified linear unit,which can be expressed using the following equation:y=max(x,0)where y is the output and x is an input value from the weighted vector.The transformed output can be supplied to a subsequent layer, such asthe hidden layer 1154, of the neural network 1150. The subsequent layerof the neural network 1150 can receive the transformed output, multiplythe transformed output by a matrix of numeric weights and anonlinearity, and provide the result to yet another layer of the neuralnetwork 1150. This process continues until the neural network 1150outputs a final result at the output layer 1156.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

Some approaches may be more efficiently and speedily executed andprocessed with machine-learning specific processors (e.g., not a genericCPU). For example, some of these processors can include a graphicalprocessing unit (GPU), an application-specific integrated circuit(ASIC), a field-programmable gate array (FPGA), a Tensor Processing Unit(TPU) by Google, and/or some other machine-learning specific processorthat implements one or more neural networks using semiconductor (e.g.,silicon (Si), gallium arsenide(GaAs)) devices.

According to embodiments discussed herein, the above-described computingdevices and systems may be utilized to implement one or more componentsof a host cluster, such as host instances, service agents, and a sharedcache, to distribute workloads in a decentralized manner. In manyembodiments, this may enable a system to provide high availability andno single point of failure. The components of the host cluster may beused to quickly and efficiently distribute and execute tasks of aworkload, resulting in a computing device and/or system with exclusiveand advantageous capabilities. For example, distributing workloads via ashared cache may improve performance and reliability of processing tasksin a workload.

In some embodiments, the above-described computing devices and systemsmay implement a service agent that collectively operate to distributeworkloads via a shared cache to achieve a decentralized load balancingsystem. In various embodiments, the workloads may be distributed basedon a customizable and dynamic scheme. For instance, the distribution ofa workload may factor in one or more parameters and/or settingsassociated with one or more components of the host cluster. In one ormore embodiments, the number and availability of service agents may varybased on workload demands. For example, service agents may periodicallydetermine (e.g., based on a timer) whether to begin to actively performtasks in a workload based on data stored in a shared cache associatedwith performance of tasks in a workload. In some such examples, theperiod of time between determining whether to begin actively performingtasks in the workload may be referred to as a service interval. Invarious examples, the service interval may include the time an activeagent performs a task, goes to sleep, and wakes up again to performanother task. In some embodiments, the number of each type of serviceagent in a host instance and/or the resources allocated to the hostinstance may be controlled. In various embodiments, the number of activeservice agents (also referred to as active agents) on each host instancemay be balanced. For instance, if one host instance implements twoactive agents and another host instance implements no active agents, thesystem may be rebalanced such that each host instance has one activeagent. In some embodiments, capabilities of different host instances,such as the capabilities of underlying hardware, may be factored intohow a workload is distributed. For instance, if a first host instancehas greater processing capabilities than a second host instance, aworkload may be balanced such that the first host instance, or serviceagent(s) implemented thereon, receive a larger portion than the secondhost instance, or service agent(s) implemented thereon. These and otherfeatures described herein may enable a computing device and/or systemimplementing one or more components of a host cluster to realize uniqueand advantageous functionalities, resulting in an improved computer.

FIG. 12 illustrates an example of an operating environment 1200 that maybe representative of various embodiments. In operating environment 1200,a host cluster 1202 may include one or more host instances 1204-1,1204-2, 1204-n (also referred to as host instances 1204). In manyembodiments, the host instances 1204 in the host cluster 1202 mayoperate to process various tasks in a workload. For example, the tasksmay be related to monitoring the health of a software system. In variousembodiments described herein, each of the host instances 1204 mayimplement one or more service agents 1206, 1208, 1210 that performdecentralized load balancing via a shared cache 1212. For instance, theservice agents may generate, monitor and/or update one or more datastructures in the shared cache 1212. In such instances, the datastructure in the shared cache 1212 may identify parameters associatedwith distribution and/or execution of tasks in a workload. In variousembodiments, host instances may each locally maintain a synchronizedcopy of the shared cached 1212. Embodiments are not limited in thiscontext.

In some embodiments, the host cluster 1202 may have multiple hostinstances or service agents sharing a set of resources (e.g., multiplesoftware components running on a single host). Some embodiments may bedesigned to ensure at least one service agent of each type is alwaysavailable to perform tasks of that type. In one or more embodiments, toachieve high availability, multiple instances of hosts may each includea set of service agents. In one or more such embodiments, the number andtype of each service agent on each host instance 1204 may be controlledto ensure back-up service agents are available to step in and provideservices (e.g., perform tasks). In some embodiments, a host cluster 1202may include one or more types of service agents. In one or moreembodiments, the load of each host instance, or service agents, may bebalanced so that a busier host instance can release at least part oftheir workload to other host instances that are less busy. In variousembodiments, distribution of tasks may be weighted. In various suchembodiments, the weight may be defined based on application. Forinstance, depending on the processing resources needed for each type oftask, each type of task may be assigned a weight. In further suchinstances, retrieving CPU usage may have a weight of one whileretrieving database metrics may have a weight of three because itrequires more load than retrieving CPU usage. In some embodiments, theweight of each application type may be based on one or more of empiricaland theoretical calculations.

In one or more embodiments, service agents may negotiate amongthemselves to determine which agent(s) from which host(s) should performtasks (as an active agent) and which agent(s) should return to a sleepstate. In one or more embodiments, once an active agent performs a taskit may update the shared cache 1212 with a corresponding timestamp. Inone or more such embodiments, the timestamp, together with other agentinfo, such as a host name, may comprise a monitoring status of aparticular agent type. In various embodiments, when a timestamp exceedsa service threshold (e.g., predetermined time interval) or one hostinstance has too many active agents, a secondary agent may transitionfrom a sleep state to an active state to achieve dynamic failover andload balancing. For example, the service threshold may include a timeequal to twice a service interval. In such examples, the serviceinterval may include an amount of time between initialization of a timerand expiration of the timer.

In some embodiments, the secondary agent may establish itself as anactive agent in the shared cache 1212. In some such embodiments,establishing the secondary agent as the active agent may cause anotheragent to transition from an active state to a sleep state. In someembodiments, capabilities of different host instances, such as thecapabilities of underlying hardware, may be factored into how a workloadis distributed. For instance, if a first host instance has greaterprocessing capabilities than a second host instance, a workload may bebalanced such that the first host instance, or service agent(s)implemented thereon, receive a larger portion than the second hostinstance, or service agent(s) implemented thereon.

In various embodiments described herein, operation of one or moreservice agents in a host cluster may include one or more of thefollowing operations described with respect to host cluster 1202 and oneor more components thereof. In some embodiments, a first timerassociated with service agent 1206-1 of the set of service agentsimplemented by host instance 1204-1. In some such embodiments, serviceagent 1206-1 may awake in response to expiration of the first timer. Inone or more embodiments, service agent 1206-1 may determine, via a localcopy of shared cache 1212 that agent 1206 is not an active agentassociated with a first task type. In various embodiments, service agent1206-1 may identify a value of a first timestamp in shared cache 1212associated with performance of a task of the first task type by serviceagent 1208-1 of host instance 1204-2 as an active agent.

In some embodiments, service agent 1206-1 may determine a time elapsedbetween the value of the first timestamp and a current time. Inembodiments in which service agent 1206-1 determines the time elapsedsince the first timestamp exceeds a service threshold, service agent1206-1 may update shared cache 1212 to indicate it is an active agentfor the service or task type and/or update the value of the firsttimestamp. In some embodiments, service agent 1206-1 may identify anumber of active agents associated with host instance 1204-2 when thetime elapsed is below the service threshold. In some such embodiments,the service threshold may include a maximum amount of time betweenperformance of two tasks of the same task type to maintain a targetservice level.

In various embodiments, service agent 1206-1 of host instance 1204-1 mayenter a sleep state when the number of active agents associated withhost instance 1204-2 is below a load difference threshold. In varioussuch embodiments, the load difference threshold may include a maximumdifference between the number of service agents in the active state onhost instance 1204-2 and host instance 1204-1. In one or moreembodiments, when the number of active agents associated with the hostinstance 1204-2 is above the load difference threshold, service agent1206-1 of host instance 1204-1 may replace a service agent on hostinstance 1204-2 with itself, as an active agent for the first task typein shared cache 1212. In one or more such embodiments, this may reducethe number of service agents in the active state implemented by hostinstance 1204-2, thereby balancing the workload. In some embodiments,service agent 1206-1 may subsequently update the value of the firsttimestamp and perform a task of the first task type in response to beinga service agent in the active state for the first task type.

FIGS. 13A and 13B illustrate examples of operating environments 1300A,1300B that may be representative of various embodiments. Operatingenvironment 1300A may include shared cache 1312 and operatingenvironment 1300B may include shared cache 1362. In many embodiments,shared cache 1212 may be the same or similar to one or more of sharedcaches 1312, 1362. In one or more embodiments described herein, serviceagents in a host cluster may generate, monitor, and/or update one ormore data structures that are conceptually the same or similar to thoseillustrated in one or more of shared caches 1312, 1362 to performdecentralized load balancing. In various embodiments, a data structuremay refer to the organization of data to suit a specific purpose (e.g.,decentralized load distribution) such that the data can be accessed andworked with in appropriate ways. In some embodiments, the datastructures may indicate one or more parameters associated withdistribution and/or execution of tasks in a workload. In operatingenvironment 1300A, shared cache 1312 may include a data structure foractive agents 1302. In operating environment 1300B, shared cache 1362may include data structures for active agents 1352 and a data structurefor a task queue 1365. Embodiments are not limited in this context.

Referring to FIG. 13A, the active agents 1302 data structure may includean entry for one or more different task types 1304-1, 1304-2, 1304-n(also referred to as task types 1304) associated with a workload. Invarious embodiments, a task type may include any type of task that isrepeated periodically, such as retrieving one or more of metric data ofresources, component availability, a process log, and the like. In manyembodiments, each task type may include a monitoring status for aparticular agent type. In many such embodiments, the monitoring statusmay include one or more time stamps together with active agent and/oractive agent host info. In some embodiments, each task type may includean indication of one or more active agents associated with therespective task type. For example, task type 1304-1 may include anindication of one or more active agents 1306-1 associated with task type1304-1, such as one or more of service agents 1206, 1208, 1210.

In various embodiments, each task type may include an indication of oneor more active agent hosts associated with the respective task type. Invarious such embodiments, active agent hosts may refer to one or more ofhost instances 1204 that have one or more active service agentsassociated with a respective task type. For instance, task type 1304-1may include an indication of one or more active agent hosts 1308-1associated with task type 1304-1, such as one or more of host instances1204. In many embodiments, each task type may include one or moretimestamps 1310 associated with the respective task type. In suchembodiments, the timestamps may indicate when a task of the respectivetask type was last performed. For example, whenever an active agentperforms a task, it may update the respective timestamp in the sharedcache.

Referring to FIG. 13B, the active agents 1352 data structure may includean entry for one or more different task types 1354-1, 1354-2, . . .1354-n (also referred to as task types 1354) associated with a workload.In the illustrated embodiment, the workload may comprise task queue1365. In various embodiments, each task type may include indications ofone or more active agents associated with the respective task type. Insome embodiments, each indication of an active agent may also identifythe host of the active agent. For example, the indication may include aunique identifier that identifies a service agent and its host instance.In one or more embodiments, the timestamps 1360 may be the same orsimilar to timestamps 1310.

In some embodiments, the task queue 1365 may include one or more tasks1352-1, 1352-2, . . . 1352-n. For instance, the tasks 1352 may beassociated with monitoring the health of a software system. In manyembodiments, a workload may include the one or more tasks 1352 in taskqueue 1365. In one or more embodiments, each task 1352 in task queue1365 may include an indication of type. In various embodiments, a sharedcache may include one or more task queues. For example, a separate taskqueue may be provided for each type of task 1352-1. In such examples,the one or more task queues could be incorporated into active agents1352 data structure, such as in respective task types 1354.

In one or more embodiments, host cluster 1202 may operate according toone or more preferences or weightings. In one or more such embodiments,the shared cache may include a data structure associated with thepreferences or weightings. In various embodiments, the preferences orweights may be utilized to enable host cluster 1202 to operate in one ormore of the following ways. In some embodiments, the number of activeservice agents on each host instance may be balanced. For instance, ifone host instance implements two active agents and another host instanceimplements no active agents, the system may be rebalanced such that eachhost instance has one active agent.

In many embodiments, one or more agents, host instances, or task typesmay be weighted differently when balancing a workload. In someembodiments, distribution of tasks and/or resources among host instancesmay be dynamically controlled. In some such embodiments, the amount ofeach type of service agent implemented by, or the portions of tasksassigned to each host instance, may be determined based on one or moreweighting conditions. For instance, if a first host instance has agreater weight than a second host instance, a workload may be balancedsuch that the first host instance, or service agent(s) implementedthereon, receive a larger portion than the second host instance, orservice agent(s) implemented thereon. In some instances, more serviceagents may be implemented some host instances. Similarly, in variousembodiments, requirements of different tasks or their frequency may befactored into how a workload is distributed.

In one or more embodiments, different resources may be provisioned tovarious service agents. In some embodiments, capabilities or processingcapabilities of different host instances, such as the capabilities ofunderlying hardware, may be factored into how a workload is distributed.For instance, if a first host instance has greater processingcapabilities than a second host instance, a workload may be balancedsuch that the first host instance, or service agent(s) implementedthereon, receive a larger portion than the second host instance, orservice agent(s) implemented thereon. In such instances, greaterprocessing capabilities may result from a host with one or more of afaster CPU, more CPU cores, or more memory.

In some embodiments, processing capabilities of hardware resourcesavailable to the first host instance may be compared to processingcapabilities of hardware resources available to the second host instanceto determine the load difference threshold. For example, if theprocessing capabilities of hardware resources available to the firsthost instance are equal to the processing capabilities of the hardwareresources available to the second host, the load difference thresholdmay be determined to be zero. In another example, if the processingcapabilities of hardware resources available to the first host instanceare greater to the processing capabilities of the hardware resourcesavailable to the second host, the load difference threshold may bedetermined to be greater than zero. In yet another example, if theprocessing capabilities of hardware resources available to the firsthost instance are less than the processing capabilities of the hardwareresources available to the second host, the load difference thresholdmay be determined to be less than zero.

FIG. 14 illustrates an example of an operating environment 1400 that maybe representative of various embodiments. In operating environment 1400,service agents 1206, 1208, . . . 1210 of host cluster 1202 may begrouped according to the task type 1304 they perform. In one or moreembodiments, an active agent 1404 may be associated with at least oneservice agent of each task type 1304. In some embodiments, shared cache1212 may include a data structure that maps service agents and/or activeagents 1404 by task type 1304. In one or more embodiments describedherein, service agents may generate, monitor and/or update one or moredata structures in the shared cache 1212 to coordinate with otherservice agents to determine one or more active agents for theirrespective task type. In one or more such embodiments, this may enablehost cluster 1202 to perform decentralized load balancing. In variousembodiments, service agents identified as active agents may be in ortransition to an active state, and service agents not identified asactive agents may be in or transition to a sleep state. In someembodiments, inactive agents may be either implicitly or explicitlyidentified in the shared cache. Embodiments are not limited in thiscontext.

FIG. 15 illustrates an example embodiment of a logic flow 1500. Thelogic flow 1500 may be representative of some or all of the operationsexecuted by one or more embodiments described herein. More specifically,the logic flow 1500 may illustrate operations performed by one or morecomponents of host cluster 1202, such as host instances and/or serviceagents. In one or more embodiments, these operations may be performed inconjunction with decentralized load balancing of tasks in a workload.Embodiments are not limited in this context.

In the illustrated embodiment shown in FIG. 15, the logic flow 1500 maybegin at block 1502. At block 1902, a timer may be initialized. Thelogic flow 1500 may enter a processing loop when transitioning to block1504. At block 1504, a service agent may be in or transition to a sleepstate. For instance, a service agent may be initialized and placed intoa sleep state. In another example, a service agent may transition to orremain in a sleep state in response to determining it is not needed forprocessing tasks in a workload based on one or more data structures in ashared cache.

Continuing to block 1506, a service agent may wake upon expiration ofthe timer. In various embodiments, the timer may enable a service agentto periodically monitor a shared cache to determine whether to beginprocessing tasks. In one or more embodiments, each service agent in hostcluster 1202 may be associated with a separate timer. In one or moresuch embodiments, the duration of the timers may be controlled and/oradjusted. In various embodiments, initialization of the timers may bestaggered. Proceeding to block 1508, upon waking, the service agent maydetermine whether an active agent is identified in a shared cache forthe type of task performed by the service agent. For instance, serviceagent 1208-1 may determine an active agent 1404-1 is identified for tasktype 1304-1. If an active agent is identified in the shared cache, logicflow 1500 may proceed to block 1510. At block 1510, the service agentmay determine whether it is the active agent identified in the sharedcache. For instance, the service agent may access one or more datastructures in the shared cache to determine one or more service agentsidentified as active for their respective task type. If the serviceagent identifies itself as an active agent indicated in the sharedcache, logic flow 1500 may proceed to block 1512.

At block 1512, the service agent may set a timestamp in the shared cacheassociated with execution of a task by an active agent. For instance,the timestamp may indicate the last time the service agent performed atask. In such instances, the timestamp may be used to calculate how muchtime has elapsed since the last time the task was performed. Proceedingto block 1514, a task may be performed by the service agent. Thus, inthe illustrated embodiments, the timestamp may be associated with whenperformance of a task was initiated. However, in some embodiments, thetimestamp may be set or updated after performance of a task. In stillfurther embodiments, the timestamp may be updated before and afterexecution of a task. In other embodiments, the shared cache may includeseparate timestamps. For instance, a first timestamp may be associatedwith initiation of a task and a second timestamp may be associated withcompletion of a task.

Continuing to block 1516, the service agent may reset the timer andtransition to a sleep state at block 1504. In various embodiments, theservice agent may only reset the timer and enter a sleep state when nomore tasks for the respective task type are awaiting execution, such asin task queue 1365. Referring back to block 1508, if an active agent isnot identified in the shared cache, the logic flow 1500 may continue toblock 1518. At block 1518, the service agent may set itself as one ormore active agents indicated in the shared cache. Next, the logic flow1500 may proceed to block 1512 and proceed as described above.

Referring back to block 1510, if the service agent determines it is notidentified as an active agent for the respective task type, the logicflow 1500 may proceed to block 1520. At block 1520, the service agentmay determine a time elapsed since a timestamp exceeds a servicethreshold. In some embodiments, the service agent may compare one ormore timestamps associated with the respective task type in the sharedcache to one or more service thresholds. If the time elapsed exceeds theservice threshold, the logic flow 1500 may proceed to block 1518 andproceed as described above. However, if the time elapsed does not exceedthe service threshold, the logic flow 1500 may proceed to block 1522.

At block 1522, the service agent may determine whether the hostinstances of the active agents identified in the shared cache have anumber of active agents that exceeds a load difference threshold. Insome embodiments, the load difference threshold may include a maximumdifference between the number of active agents on the host instance ofthe service agent and the number of active agents of the host instanceidentified in the shared cache. If the number of active agents of thehost instance that includes the active agent exceeds the load differencethreshold, the logic flow 1500 may proceed to block 1518. In variousembodiments, if the number of active agents of the host instance thatincludes the active agent exceeds the load difference threshold, theservice agent may replace or remove the indication in the shared cachethat identifies the other service agent as an active agent for therespective task type. However, if the number of active agents of thehost instance that includes the active agent does not exceeds the loaddifference threshold, the logic flow 1500 may proceed to block 1516, andproceed as described above.

FIGS. 16A and 16B illustrate exemplary states 1600A, 1600B of a hostinstance 1602 that may be representative of various embodiments. In theillustrated embodiments, host instance 1602 may include service agents1610, 1620, 1630, 1640. In FIGS. 16A and 16B, service agents with anarrow pointing toward the bottom of the page (e.g., service agents instate 1600A) may indicate the service agent is in a sleep state, andservice agents with an arrow pointing toward the top of the page (e.g.,service agents in state 1600B may indicate the service agent is in anactive state. In the illustrated embodiments, each of the service agentsof the host instance 1602 may be associated with a different task type.In state 1600A, each service agent of host instance 1602 may be in asleep state, such as in response to initialization or a previousdetermination that it was not needed to perform tasks. In variousembodiments, upon expiration of a timer, each service agent maydetermine to set themselves as an active agent for their respective tasktypes in a shared cache, as shown in state 1600B of host instance 1602.Embodiments are not limited in this context.

FIGS. 17A-17C illustrate exemplary states 1700A, 1700B, 1700C of hostinstances 1602, 1702 that may be representative of various embodiments.In the illustrated embodiments, host instance 1602 may include serviceagents 1610, 1620, 1630, 1640 and host instance 1702 may include serviceagents 1710, 1720, 1730, 1740. In some embodiments, states 1700A, 1700B,1700C may occur subsequent to states 1600A, 1600B discussed previously.Similar to FIGS. 16A and 16B, in FIGS. 17A-17C, service agents with anarrow pointing toward the bottom of the page may indicate the serviceagent is in a sleep state and service agents with an arrow pointingtoward the top of the page may indicate the service agent is in anactive state. In one or more embodiments described herein, FIGS. 17A-17Cmay illustrate various states associated with balancing the number ofactive agents among different host instances 1602, 1702. In one or moresuch embodiments, this may be based on comparing the difference betweena number of service agents in the active state on host instance 1602 anda number of service agents in the active state on host instance 1702 toa load difference threshold. Embodiments are not limited in thiscontext.

In some embodiments, in states 1700A, 1700B, 1700C, only one activeagent of each task type may be allowed at a time. In the illustratedembodiments, service agents 1610, 1710 may be associated with the sametask type, service agents 1620, 1720 may be associated with the sametask type, service agents 1630, 1730 may be associated with the sametask type, and service agents 1640, 1740 may be associated with the sametask type. Additionally, the task type associated with each serviceagent in a respective host instance may be different. In variousembodiments, host instances 1602, 1702 may have the same processingcapabilities.

Referring to FIG. 17A, state 1700A may include all service agents ofhost instance 1602 being in an active state and identified as activeagents in a shared cache and all service agents of host instance 1702being in a sleep state. In state 1700A, each service agent of hostinstance 1702 may be in a sleep state in response to initialization or aprevious determination the service agent was not needed to perform tasksin a workload. In some embodiments, service agent 1720 of host instance1702 may awaken and determine the number of active service agentsimplemented by host instance 1602 and identified in the shared cacheresults in a load difference threshold being exceeded. For example, ifthe load difference threshold is zero, service agent 1720 may calculatethe load difference threshold of host instance 1602 as follows. Sincehost instance 1602 has four active service agents and host instance 1702has zero active service agents, the difference between active serviceagents associated with host instance 1602 and host instance 1702 may befour since 4−0=4. Accordingly, the load difference threshold of zero isexceeded.

Proceeding to state 1700B of FIG. 17B, in response to determining theload difference threshold is exceeded, service agent 1720 may update theshared cache to identify itself instead of service agent 1620 as theactive agent for the respective task type. In such embodiments, serviceagent 1620 may transition to a sleep state in response to being replacedas the active service agent. In some embodiments, service agent 1740 ofhost instance 1702 may awaken (e.g., transition from a sleep state to anactive state) and determine the number of active service agentsimplemented by host instance 1602 and identified in the shared cachestill results in the load difference threshold being exceeded. Forexample, if the load difference threshold is zero, service agent 1740may calculate the load difference threshold of host instance 1602 asfollows. Since host instance 1602 has three active service agents andhost instance 1702 has one active service agent, the difference betweenactive service agents associated with host instance 1602 and hostinstance 1702 may be two since 3−1=2. Accordingly, the load differencethreshold of zero is exceeded.

Continuing to state 1700C of FIG. 17C, in response to determining theload difference threshold is exceeded, service agent 1740 may update theshared cache to identify itself instead of service agent 1640 as theactive agent for the respective task type. In such embodiments, serviceagent 1640 may transition to a sleep state in response to being replacedas the active service agent. Subsequently, in various embodiments,service agent 1710 of host instance 1702 may awaken and determine thenumber of active service agents implemented by host instance 1602 andidentified in the shared cache no longer results in the load differencethreshold being exceeded. For example, if the load difference thresholdis zero, service agent 1710 may calculate the load difference thresholdof host instance 1602 as follows. Since host instance 1602 has twoactive service agents and host instance 1702 has two active serviceagents, the difference between active service agents associated withhost instance 1602 and host instance 1702 may be zero since 2−2=0.Accordingly, the load difference threshold of zero is not exceeded. Inresponse to determining the load difference threshold is not exceeded,service agent 1710 may return to a sleep state.

FIGS. 18A and 18B illustrate an embodiment of a logic flow 1800. Thelogic flow 1800 may be representative of some or all of the operationsexecuted by one or more embodiments described herein. More specifically,the logic flow 1800 may illustrate operations performed by one or morecomponents of host cluster 1202, such as host instances 1204 and/orservice agents. In one or more embodiments, these operations may beperformed in conjunction with generating, monitoring, and/or updating ashared cache to perform decentralized load balancing. Embodiments arenot limited in this context.

In the illustrated embodiment shown in FIG. 18A, the logic flow 1800 maybegin at block 1802. At block 1802, a first timer associated with afirst service agent in a set of service agents implemented by a firsthost instance may be initialized, the set of service agents comprisingtwo or more service agents, wherein each service agent in the set ofservice agents is in a state of one or more states, the one or morestates including a sleep state and an active state. For instance, atimer associated with service agent 1206-1 may be initialized. In someinstances, service agents 1610, 1620, 1630, 1640 may be in the activestate while service agents 1710, 1720, 1730, 1740 are in the sleepstate. Proceeding to block 1804, the first service agent may transitionfrom the sleep state to the active state in response to expiration ofthe first timer. For instance, service agent 1610 may transition to theactive state in response to expiration of a timer (see e.g., FIGS. 16Aand 16B).

Continuing to block 1806, the first service agent may be determined notto be one of one or more agents in the active state for a first tasktype based on a data structure in the shared cache that indicates theone or more agents in the active state for the first task type, theshared cache accessible to a cluster of two or more host instances, thecluster of host instances including the first host instance and a secondhost instance. For instance, service agent 1206-2 may access sharedcache 1312 to determine it is not an active agent (e.g., active agent1306-1). At block 1808, a value of a first timestamp of one or moretimestamps in the shared cache may be identified based on an associationof the first timestamp with the first task type in the shared cache, thefirst timestamp associated with performance of a task of the first tasktype by one of the one or more agents in the active state for the firsttask type and implemented by the second host instance. For instance, thevalue of timestamp 1310-1 may be identified based on its associationwith task type 1304-1 (see e.g., FIG. 13A). At block 1810 a time elapsedbetween the value of the first timestamp and a current time may bedetermined. For example, a time elapsed between the value of thetimestamp 1310-1 and a current time may be determined by service agent1208-1.

Proceeding to block 1812, wherein when the time elapsed is at or above aservice threshold, the processor to perform operations comprising setthe first service agent as one of the one or more agents in the activestate for the first task type in the data structure in the shared cacheand update the value of the first timestamp associated with the firsttask type in the shared cache. For instance, service agent 1730 mayupdate shared cache 1362 such that task type 1354-1 indicates serviceagent 1730 in active agent(s) 1356-1 and update a timestamp oftimestamp(s) 1360-1. Continuing to block 1814, wherein when the timeelapsed is at or below the service threshold, the processor to performoperations comprising identify a number of agents in the active stateassociated with the second host instance via the shared cache. Forinstance, the number of active service agents associated with hostinstance 1204-n may be determined. It will be appreciated that althoughthe logic flow 1800 utilizes the conditionals ‘above the servicethreshold’ and ‘at or below the service threshold’ any conditionalsmaybe used in a comparison without departing from the scope of thisdisclosure. For example, the conditionals may include ‘at or above theservice threshold’ and ‘below the service threshold’.

Going on to block 1816, wherein when the time elapsed is at or below theservice threshold, the processor to perform operations comprising placethe first service agent in the sleep state when a difference between thenumber of agents in the active state associated with the second hostinstance and a number of agents in the active state associated with thefirst host instance is at or below a load difference threshold. Forexample, service agent 1710 may be placed in a sleep state when adifference between the number of agents in the active state associatedwith the second host instance and a number of agents in the active stateassociated with the first host instance is at or below a load differencethreshold. Proceeding to block 1818, wherein when the time elapsed is ator below the service threshold, the processor to perform operationscomprising set the first service agent as one of the one or more agentsin the active state for the first task type in the shared cache andupdate the value of the first timestamp in the shared cached when thedifference between the number of agents in the active state associatedwith the second host instance and the number of agents in the activestate associated with the first host instance is above the loaddifference threshold. For instance, active agent(s) 1356-n andtimestamp(s) 1360-n of task type 1354-n in shared cached 1362 may beupdated. It will be appreciated that although the logic flow 1900utilizes the conditionals ‘at or below the load difference threshold’and ‘above the load difference threshold’ any conditionals maybe used ina comparison without departing from the scope of this disclosure. Forexample, the conditionals may include ‘at or below the load differencethreshold’ and ‘above the load difference threshold’.

FIG. 19 illustrates an embodiment of a logic flow 1900. The logic flow1900 may be representative of some or all of the operations executed byone or more embodiments described herein. More specifically, the logicflow 1900 may illustrate operations performed by one or more componentsof host cluster 1202, such as host instances 1204 and/or service agents.In one or more embodiments, these operations may be performed inconjunction with generating, monitoring, and/or updating a shared cacheto perform decentralized load balancing. Embodiments are not limited inthis context.

In the illustrated embodiment shown in FIG. 19, the logic flow 1900 maybegin at block 1902. At block 1902, one or more service agents with eachhost instance in a cluster having a plurality of host instances, the oneor more service agents to perform tasks included in a workload. Forexample, the service agents of host cluster 1202 may perform tasksincluded in a workload. Continuing to block 1904, a shared cached may beutilized by the one or more service agents of the plurality of hostsinstances to automatically balance processing of the tasks in theworkload among the host instances in the cluster without a centralizedcontroller or a load balancer. For instance, shared cached 1362 may beutilized by one or more service agents in host cluster 1202.

Proceeding to block 1906, a task in the workload may be performed with afirst service agent based on a data structure in the shared cache thatindicates the first service agent is an active agent for the task, thefirst service agent of the one or more service agents implemented by afirst host instance in the cluster of host instances. For example,service agent 1208-n may perform a task based on a data structure inshared cache 1212. At block 1908, a second service agent may beimplemented with a second host instance in the cluster of hostinstances, the second service agent to transition from a sleep state toan active state in response to expiration of a timer. For instance,service agent 1210-1 may be implemented by host instance 1204-n. In suchinstance, service agent 1210-1 may transition from a sleep state to anactive state in response to expiration of a timer.

Continuing to block 1910, a number of active service agents associatedwith the first host instance may be identified with the second serviceagent based on the data structure in the shared cache. For example, thenumber of active service agent associated with host instance 1204-2 maybe identified by service agent 1210-1 of host instance 1204-n based onshared cached 1212. At block 1912, a difference between the number ofactive service agents associated with the first host instance and anumber of active service agents associated with the second host instancemay be computed by the second service agent. For instance, service agent1210-1 may determine the difference in the number of active serviceagents associated with host instance 1204-2 and host instance 1204-n.

At block 1914, the difference between the number of active serviceagents associated with the first host instance and the number of activeservice agents associated with the second host instance may be comparedto a load difference threshold by the second service agent. Forinstance, service agent 1210-1 may compare the difference to a loaddifference threshold between host instance 1204-2 and host instance1204-n. In some embodiments, the load difference threshold may be storedin the shared cache. Continuing to block 1916, the data structure in theshared cache may be updated by the second service agent to indicate thatthe second service agent is the active agent for the task to reduce aportion of the workload performed by the first service agent of the oneor more service agents implemented by the first host instance in thecluster of host instances when the difference between the number ofactive service agents associated with the first host instance and thenumber of active service agents associated with the second host instanceexceeds the load difference threshold. For example, service agent 1210-1may update shared cache 1312 to indicate that service agent 1210-1 isthe active agent for the task.

In many embodiments, one or more portions of the processing or logicflows described herein, including the components of which each iscomposed, may be selected to be operative on whatever type of processoror processors that are selected to implement one or more of hostinstances 1204. For instance, these may include any of a wide variety ofcommercially available processors. Further, one or more of theseprocessors may include multiple processors, a multi-threaded processor,a multi-core processor (whether the multiple cores coexist on the sameor separate dies), and/or a multi-processor architecture of some othervariety by which multiple physically separate processors are linked.

In various embodiments, one or more processors and/or devices used toimplement portions of the processing or logic flows described herein maybe selected to efficiently perform one or more operations describedherein. In some embodiments, one or more operations described herein maybe performed at least partially in parallel. By way of example,processors may incorporate a single-instruction multiple-data (SIMD)architecture, may incorporate multiple processing pipelines, and/or mayincorporate the ability to support multiple simultaneous threads ofexecution per processing pipeline.

In some embodiments, each of these one or more portions of theprocessing or logic flows described herein may include one or more of anoperating system, device drivers and/or application-level routines(e.g., so-called “software suites” provided on disc media, “applets”obtained from a remote server, etc.). Where an operating system isincluded, the operating system may be any of a variety of availableoperating systems appropriate for the processing or logic circuitry.Where one or more device drivers are included, those device drivers mayprovide support for any of a variety of other components, whetherhardware or software components, described herein.

In various embodiments, one or more components of host cluster 1202(e.g., host instance 1204-1, 1204-2, 1204-n) may utilize or includestorage and/or memory (e.g., shared cache 1212). In various suchembodiments, the storage and/or memory may be based on any of a widevariety of information storage technologies, including volatiletechnologies requiring the uninterrupted provision of electric power,and/or including technologies entailing the use of machine-readablestorage media that may or may not be removable. Thus, each of thesestorages may include any of a wide variety of types (or combination oftypes) of storage device, including without limitation, read-only memory(ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data-RateDRAM (DDR-DRAM), synchronous DRAM (SDRAM), static RAM (SRAM),programmable ROM (PROM), erasable programmable ROM (EPROM), electricallyerasable programmable ROM (EEPROM), flash memory, polymer memory (e.g.,ferroelectric polymer memory), ovonic memory, phase change orferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS)memory, magnetic or optical cards, one or more individual ferromagneticdisk drives, non-volatile storage class memory, or a plurality ofstorage devices organized into one or more arrays (e.g., multipleferromagnetic disk drives organized into a Redundant Array ofIndependent Disks array, or RAID array). It should be noted thatalthough each of these storages is depicted as a single block, one ormore of these may include multiple storage devices that may be based ondiffering storage technologies. Thus, for example, one or more of eachof these depicted storages may represent a combination of an opticaldrive or flash memory card reader by which programs and/or data may bestored and conveyed on some form of machine-readable storage media, aferromagnetic disk drive to store programs and/or data locally for arelatively extended period, and one or more volatile solid-state memorydevices enabling relatively quick access to programs and/or data (e.g.,SRAM or DRAM). It should also be noted that each of these storages maybe made up of multiple storage components based on identical storagetechnology, but which may be maintained separately as a result ofspecialization in use (e.g., some DRAM devices employed as a mainstorage while other DRAM devices employed as a distinct frame buffer ofa graphics controller). However, in one or more embodiments, storageand/or memory of one or more of the node may be implemented with aredundant array of independent discs (RAID) of a RAID level selected toprovide fault tolerance to prevent loss of one or more of these datasetsand/or to provide increased speed in accessing one or more of thesedatasets.

In various embodiments, one or more of the interfaces described hereinmay each utilize or include any of a variety of types of input devicethat may each employ any of a wide variety of input detection and/orreception technologies. Examples of such input devices include, and arenot limited to, microphones, remote controls, stylus pens, card readers,finger print readers, virtual reality interaction gloves, graphicalinput tablets, joysticks, keyboards, retina scanners, the touch inputcomponents of touch screens, trackballs, environmental sensors, and/oreither cameras or camera arrays to monitor movement of persons to acceptcommands and/or data provided by those persons via gestures and/orfacial expressions. Various embodiments may include or utilize one ormore displays to present information. In various such embodiments, eachof the displays may each be any of a variety of types of display devicethat may each employ any of a wide variety of visual presentationtechnologies. Examples of such a display device includes, and is notlimited to, a cathode-ray tube (CRT), an electroluminescent (EL) panel,a liquid crystal display (LCD), a gas plasma display, etc. In someembodiments, one or more of the interfaces may be a touchscreen display.

Some embodiments may include one or more network interfaces that employany of a wide variety of communications technologies enabling thesedevices to be coupled to one or more other devices. Each of theseinterfaces includes circuitry providing at least some of the requisitefunctionality to enable such coupling. However, each of these interfacesmay also be at least partially implemented with sequences ofinstructions executed by corresponding ones of the processors (e.g., toimplement a protocol stack or other features). Where electrically and/oroptically conductive cabling is employed, these interfaces may employtimings and/or protocols conforming to any of a variety of industrystandards, including without limitation, RS-232C, RS-422, USB, Ethernet(IEEE-802.3) or IEEE-1394. Where the use of wireless transmissions isentailed, these interfaces may employ timings and/or protocolsconforming to any of a variety of industry standards, including withoutlimitation, IEEE 802.11a, 802.11ad, 802.11ah, 802.11ax, 802.11b,802.11g, 802.16, 802.20 (commonly referred to as “Mobile BroadbandWireless Access”); Bluetooth; ZigBee; or a cellular radiotelephoneservice such as GSM with General Packet Radio Service (GSM/GPRS),CDMA/1×RTT, Enhanced Data Rates for Global Evolution (EDGE), EvolutionData Only/Optimized (EV-DO), Evolution For Data and Voice (EV-DV), HighSpeed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access(HSUPA), 4G LTE, etc. However, in a specific embodiment, a networkinterface may be implemented with multiple copper-based or fiber-opticbased network interface ports to provide redundant and/or parallelpathways in exchanging data.

In various embodiments, the processing, memory, and/or storage resourcesof host cluster 1202 may be divided among the multiple systems (e.g.,host instances 1204-1, 1204-2, 1204-n). In various such embodiments, oneor more API architectures may support communications among the multiplesystems. The one or more API architectures may be configured to and/orselected to conform to any of a variety of standards for distributedprocessing, including without limitation, IEEE P2413, AllJoyn, IoTivity,etc. By way of example, a subset of API and/or other architecturalfeatures of one or more of such standards may be employed to implementthe relatively minimal degree of coordination described herein toprovide greater efficiency in parallelizing processing of data, whileminimizing exchanges of coordinating information that may lead toundesired instances of serialization among processes. However, it shouldbe noted that the parallelization of storage, retrieval and/orprocessing of data among multiple systems is not dependent on, norconstrained by, existing API architectures and/or supportingcommunications protocols. More broadly, there is nothing in the mannerin which the data may be organized in storage, transmission, and/ordistribution via network interface that is bound to existing APIarchitectures or protocols.

Some systems may use Hadoop®, an open-source framework for storing andanalyzing big data in a distributed computing environment. Some systemsmay use cloud computing, which can enable ubiquitous, convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, servers, storage, applications and services)that can be rapidly provisioned and released with minimal managementeffort or service provider interaction. Some grid systems may beimplemented as a multi-node Hadoop® cluster, as understood by a personof skill in the art. Apache™ Hadoop® is an open-source softwareframework for distributed computing.

The invention claimed is:
 1. A system comprising: a cluster having aplurality of host instances, each host instance in the cluster, includesa processor and memory, to implement one or more service agents, the oneor more service agents to perform tasks included in a workload; a sharedcache utilized by the one or more service agents of the plurality ofhosts instances to automatically balance processing of the tasks in theworkload among the host instances in the cluster without a centralizedcontroller or a load balancer; a first service agent of the one or moreservice agents implemented by a first host instance in the cluster ofhost instances to perform a task in the workload based on a datastructure in the shared cache that indicates the first service agent isan active agent for the task; a second service agent of the one or moreservice agents implemented by a second host instance in the cluster ofhost instances to: transition from a sleep state to an active state inresponse to expiration of a timer; identify a number of active serviceagents associated with the first host instance based on the datastructure in the shared cache; compute a difference between the numberof active service agents associated with the first host instance and anumber of active service agents associated with the second hostinstance; compare the difference between the number of active serviceagents associated with the first host instance and the number of activeservice agents associated with the second host instance to a loaddifference threshold, wherein the load difference threshold is anumerical value determined based on a comparison of processingcapabilities of hardware resources available to the first host instanceto processing capabilities of hardware resources available to the secondhost instance; and update the data structure in the shared cache toindicate that the second service agent is the active agent for the taskwhen the difference between the number of active service agentsassociated with the first host instance and the number of active serviceagents associated with the second host instance exceeds the loaddifference threshold, the update to the data structure in the sharedcache to reduce a portion of the workload performed by the first serviceagent of the one or more service agents implemented by the first hostinstance in the cluster of host instances.
 2. The system of claim 1,wherein the first host instance and the second host instance have localaccess to a synchronized copy of the shared cached.
 3. The system ofclaim 1, wherein the second service agent initializes a timer andtransitions into the sleep state when the difference between the numberof active service agents associated with the first host instance and thenumber of active service agents associated with the second host instancedoes not exceed the load difference threshold.
 4. The system of claim 1,wherein the second service agent performs a second task in the workloadand updates a timestamp in the shared cached to a current time based onperformance of the second task.
 5. The system of claim 4, wherein thedata structure includes an association between the timestamp and a typeof the task.
 6. The system of claim 1, the first service agentcomprising a metric collecting agent and wherein the task includes aportion of a resource metric collection service that monitors dynamicmetrics associated with health of a software system.
 7. The system ofclaim 1, wherein the first service agent updates a timestamp in theshared cached to indicate completion of the task.
 8. The system of claim1, wherein the first service agent identifies the task for performancebased on a task queue in the shared cache.
 9. The system of claim 1,wherein the load difference is set to zero based on the comparisonindicating the processing capabilities of the hardware resourcesavailable to the first host instance are equal to the processingcapabilities of the hardware resources available to the second hostinstance.
 10. A computer-implemented method, comprising: implementingone or more service agents with each host instance in a cluster having aplurality of host instances, the one or more service agents to performtasks included in a workload; utilizing a shared cache with the one ormore service agents of the plurality of hosts instances to automaticallybalance processing of the tasks in the workload among the host instancesin the cluster without a centralized controller or a load balancer;performing a task in the workload with a first service agent based on adata structure in the shared cache that indicates the first serviceagent is an active agent for the task, the first service agent of theone or more service agents implemented by a first host instance in thecluster of host instances; implementing a second service agent of theone or more service agents with a second host instance in the cluster ofhost instances, the second service agent for: transitioning from a sleepstate to an active state in response to expiration of a timer;identifying a number of active service agents associated with the firsthost instance based on the data structure in the shared cache; computinga difference between the number of active service agents associated withthe first host instance and a number of active service agents associatedwith the second host instance; comparing the difference between thenumber of active service agents associated with the first host instanceand the number of active service agents associated with the second hostinstance to a load difference threshold, wherein the load differencethreshold is a numerical value determined based on a comparison ofprocessing capabilities of hardware resources available to the firsthost instance to processing capabilities of hardware resources availableto the second host instance; and updating the data structure in theshared cache to indicate that the second service agent is the activeagent for the task when the difference between the number of activeservice agents associated with the first host instance and the number ofactive service agents associated with the second host instance exceedsthe load difference threshold, the update to the data structure in theshared cache to reduce a portion of the workload performed by the firstservice agent of the one or more service agents implemented by the firsthost instance in the cluster of host instances.
 11. Thecomputer-implemented method of claim 10, wherein the first host instanceand the second host instance have local access to a synchronized copy ofthe shared cached.
 12. The computer-implemented method of claim 10,wherein the second service agent initializes a timer and transitionsinto the sleep state when the difference between the number of activeservice agents associated with the first host instance and the number ofactive service agents associated with the second host instance does notexceed the load difference threshold.
 13. The computer-implementedmethod of claim 10, wherein the second service agent performs a secondtask in the workload and updates a timestamp in the shared cached to acurrent time based on performance of the second task.
 14. Thecomputer-implemented method of claim 13, wherein the data structureincludes an association between the timestamp and a type of the task.15. The computer-implemented method of claim 10, the first service agentcomprising a metric collecting agent and wherein the task includes aportion of a resource metric collection service that monitors dynamicmetrics associated with health of a software system.
 16. Thecomputer-implemented method of claim 10, wherein the first service agentupdates a timestamp in the shared cached to indicate completion of thetask.
 17. The computer-implemented method of claim 10, wherein the firstservice agent identifies the task for performance based on a task queuein the shared cache.
 18. The computer-implemented method of claim 10,wherein the load difference is set to zero based on the comparisonindicating the processing capabilities of the hardware resourcesavailable to the first host instance are equal to the processingcapabilities of the hardware resources available to the second hostinstance.
 19. A computer-program product tangibly embodied in anon-transitory machine-readable storage medium, the computer-programproduct including instructions operable to cause a processor to performoperations comprising: implement one or more service agents with eachhost instance in a cluster having a plurality of host instances, the oneor more service agents to perform tasks included in a workload; utilizea shared cache with the one or more service agents of the plurality ofhosts instances to automatically balance processing of the tasks in theworkload among the host instances in the cluster without a centralizedcontroller or a load balancer; perform a task in the workload with afirst service agent based on a data structure in the shared cache thatindicates the first service agent is an active agent for the task, thefirst service agent of the one or more service agents implemented by afirst host instance in the cluster of host instances; implement a secondservice agent of the one or more service agents with a second hostinstance in the cluster of host instances, the second service agent to:transition from a sleep state to an active state in response toexpiration of a timer; identify a number of active service agentsassociated with the first host instance based on the data structure inthe shared cache; compute a difference between the number of activeservice agents associated with the first host instance and a number ofactive service agents associated with the second host instance; comparethe difference between the number of active service agents associatedwith the first host instance and the number of active service agentsassociated with the second host instance to a load difference threshold,wherein the load difference threshold is a numerical value determinedbased on a comparison of processing capabilities of hardware resourcesavailable to the first host instance to processing capabilities ofhardware resources available to the second host instance; and update thedata structure in the shared cache to indicate that the second serviceagent is the active agent for the task when the difference between thenumber of active service agents associated with the first host instanceand the number of active service agents associated with the second hostinstance exceeds the load difference threshold, the update to the datastructure in the shared cache to reduce a portion of the workloadperformed by the first service agent of the one or more service agentsimplemented by the first host instance in the cluster of host instances.20. The computer-program product of claim 19, wherein the first hostinstance and the second host instance have local access to asynchronized copy of the shared cached.
 21. The computer-program productof claim 19, wherein the second service agent initializes a timer andtransitions into the sleep state when the difference between the numberof active service agents associated with the first host instance and thenumber of active service agents associated with the second host instancedoes not exceed the load difference threshold.
 22. The computer-programproduct of claim 19, wherein the second service agent performs a secondtask in the workload and updates a timestamp in the shared cached to acurrent time based on performance of the second task.
 23. Thecomputer-program product of claim 22, wherein the data structureincludes an association between the timestamp and a type of the task.24. The computer-program product of claim 19, the first service agentcomprising a metric collecting agent and wherein the task includes aportion of a resource metric collection service that monitors dynamicmetrics associated with health of a software system.
 25. Thecomputer-program product of claim 19, wherein the first service agentupdates a timestamp in the shared cached to indicate completion of thetask.
 26. The computer-program product of claim 19, wherein the firstservice agent identifies the task for performance based on a task queuein the shared cache.
 27. The computer-program product of claim 19,wherein the load difference is set to zero based on the comparisonindicating the processing capabilities of the hardware resourcesavailable to the first host instance are equal to the processingcapabilities of the hardware resources available to the second hostinstance.